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Franke K, Cai C, Ponder K, Fu J, Sokoloski S, Berens P, Tolias AS. Asymmetric distribution of color-opponent response types across mouse visual cortex supports superior color vision in the sky. eLife 2024; 12:RP89996. [PMID: 39234821 PMCID: PMC11377037 DOI: 10.7554/elife.89996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/06/2024] Open
Abstract
Color is an important visual feature that informs behavior, and the retinal basis for color vision has been studied across various vertebrate species. While many studies have investigated how color information is processed in visual brain areas of primate species, we have limited understanding of how it is organized beyond the retina in other species, including most dichromatic mammals. In this study, we systematically characterized how color is represented in the primary visual cortex (V1) of mice. Using large-scale neuronal recordings and a luminance and color noise stimulus, we found that more than a third of neurons in mouse V1 are color-opponent in their receptive field center, while the receptive field surround predominantly captures luminance contrast. Furthermore, we found that color-opponency is especially pronounced in posterior V1 that encodes the sky, matching the statistics of natural scenes experienced by mice. Using unsupervised clustering, we demonstrate that the asymmetry in color representations across cortex can be explained by an uneven distribution of green-On/UV-Off color-opponent response types that are represented in the upper visual field. Finally, a simple model with natural scene-inspired parametric stimuli shows that green-On/UV-Off color-opponent response types may enhance the detection of 'predatory'-like dark UV-objects in noisy daylight scenes. The results from this study highlight the relevance of color processing in the mouse visual system and contribute to our understanding of how color information is organized in the visual hierarchy across species.
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Affiliation(s)
- Katrin Franke
- Department of Ophthalmology, Byers Eye Institute, Stanford University School of Medicine, Stanford, United States
- Stanford Bio-X, Stanford University, Stanford, United States
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, United States
- Department of Neuroscience & Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, United States
| | - Chenchen Cai
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
- Graduate Training Center of Neuroscience, International Max Planck Research School, University of Tübingen, Tübingen, Germany
| | - Kayla Ponder
- Department of Neuroscience & Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, United States
| | - Jiakun Fu
- Department of Neuroscience & Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, United States
| | - Sacha Sokoloski
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
- Hertie Institute for AI in Brain Health, University of Tübingen, Tübingen, Germany
| | - Philipp Berens
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
- Hertie Institute for AI in Brain Health, University of Tübingen, Tübingen, Germany
| | - Andreas Savas Tolias
- Department of Ophthalmology, Byers Eye Institute, Stanford University School of Medicine, Stanford, United States
- Stanford Bio-X, Stanford University, Stanford, United States
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, United States
- Department of Neuroscience & Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, United States
- Department of Electrical Engineering, Stanford University, Stanford, United States
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2
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Hoshal BD, Holmes CM, Bojanek K, Salisbury J, Berry MJ, Marre O, Palmer SE. Stimulus invariant aspects of the retinal code drive discriminability of natural scenes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.08.08.552526. [PMID: 37609259 PMCID: PMC10441377 DOI: 10.1101/2023.08.08.552526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Everything that the brain sees must first be encoded by the retina, which maintains a reliable representation of the visual world in many different, complex natural scenes while also adapting to stimulus changes. This study quantifies whether and how the brain selectively encodes stimulus features about scene identity in complex naturalistic environments. While a wealth of previous work has dug into the static and dynamic features of the population code in retinal ganglion cells, less is known about how populations form both flexible and reliable encoding in natural moving scenes. We record from the larval salamander retina responding to five different natural movies, over many repeats, and use these data to characterize the population code in terms of single-cell fluctuations in rate and pairwise couplings between cells. Decomposing the population code into independent and cell-cell interactions reveals how broad scene structure is encoded in the retinal output. while the single-cell activity adapts to different stimuli, the population structure captured in the sparse, strong couplings is consistent across natural movies as well as synthetic stimuli. We show that these interactions contribute to encoding scene identity. We also demonstrate that this structure likely arises in part from shared bipolar cell input as well as from gap junctions between retinal ganglion cells and amacrine cells.
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3
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Turishcheva P, Fahey PG, Vystrčilová M, Hansel L, Froebe R, Ponder K, Qiu Y, Willeke KF, Bashiri M, Wang E, Ding Z, Tolias AS, Sinz FH, Ecker AS. The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos. ARXIV 2024:arXiv:2305.19654v2. [PMID: 37396602 PMCID: PMC10312815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Understanding how biological visual systems process information is challenging due to the complex nonlinear relationship between neuronal responses and high-dimensional visual input. Artificial neural networks have already improved our understanding of this system by allowing computational neuroscientists to create predictive models and bridge biological and machine vision. During the Sensorium 2022 competition, we introduced benchmarks for vision models with static input (i.e. images). However, animals operate and excel in dynamic environments, making it crucial to study and understand how the brain functions under these conditions. Moreover, many biological theories, such as predictive coding, suggest that previous input is crucial for current input processing. Currently, there is no standardized benchmark to identify state-of-the-art dynamic models of the mouse visual system. To address this gap, we propose the Sensorium 2023 Benchmark Competition with dynamic input (https://www.sensorium-competition.net/). This competition includes the collection of a new large-scale dataset from the primary visual cortex of ten mice, containing responses from over 78,000 neurons to over 2 hours of dynamic stimuli per neuron. Participants in the main benchmark track will compete to identify the best predictive models of neuronal responses for dynamic input (i.e. video). We will also host a bonus track in which submission performance will be evaluated on out-of-domain input, using withheld neuronal responses to dynamic input stimuli whose statistics differ from the training set. Both tracks will offer behavioral data along with video stimuli. As before, we will provide code, tutorials, and strong pre-trained baseline models to encourage participation. We hope this competition will continue to strengthen the accompanying Sensorium benchmarks collection as a standard tool to measure progress in large-scale neural system identification models of the entire mouse visual hierarchy and beyond.
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Affiliation(s)
- Polina Turishcheva
- Institute of Computer Science and Campus Institute Data Science, University of Göttingen, Germany
| | - Paul G Fahey
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Department of Ophthalmology, Byers Eye Institute, Stanford University School of Medicine, Stanford, CA, US
- Stanford Bio-X, Stanford University, Stanford, CA, US
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, US
| | - Michaela Vystrčilová
- Institute of Computer Science and Campus Institute Data Science, University of Göttingen, Germany
| | - Laura Hansel
- Institute of Computer Science and Campus Institute Data Science, University of Göttingen, Germany
| | - Rachel Froebe
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Department of Ophthalmology, Byers Eye Institute, Stanford University School of Medicine, Stanford, CA, US
- Stanford Bio-X, Stanford University, Stanford, CA, US
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, US
| | - Kayla Ponder
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
| | - Yongrong Qiu
- Institute of Computer Science and Campus Institute Data Science, University of Göttingen, Germany
- Department of Ophthalmology, Byers Eye Institute, Stanford University School of Medicine, Stanford, CA, US
- Stanford Bio-X, Stanford University, Stanford, CA, US
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, US
| | - Konstantin F Willeke
- Institute of Computer Science and Campus Institute Data Science, University of Göttingen, Germany
- Department of Ophthalmology, Byers Eye Institute, Stanford University School of Medicine, Stanford, CA, US
- Stanford Bio-X, Stanford University, Stanford, CA, US
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, US
- International Max Planck Research School for Intelligent Systems, University of Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Germany
| | - Mohammad Bashiri
- Institute of Computer Science and Campus Institute Data Science, University of Göttingen, Germany
- International Max Planck Research School for Intelligent Systems, University of Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Germany
| | - Eric Wang
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
| | - Zhiwei Ding
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
| | - Andreas S Tolias
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Department of Ophthalmology, Byers Eye Institute, Stanford University School of Medicine, Stanford, CA, US
- Stanford Bio-X, Stanford University, Stanford, CA, US
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, US
- Department of Electrical Engineering, Stanford University, Stanford, CA, US
| | - Fabian H Sinz
- Institute of Computer Science and Campus Institute Data Science, University of Göttingen, Germany
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- International Max Planck Research School for Intelligent Systems, University of Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Germany
| | - Alexander S Ecker
- Institute of Computer Science and Campus Institute Data Science, University of Göttingen, Germany
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
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4
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Eckmann S, Young EJ, Gjorgjieva J. Synapse-type-specific competitive Hebbian learning forms functional recurrent networks. Proc Natl Acad Sci U S A 2024; 121:e2305326121. [PMID: 38870059 PMCID: PMC11194505 DOI: 10.1073/pnas.2305326121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 04/25/2024] [Indexed: 06/15/2024] Open
Abstract
Cortical networks exhibit complex stimulus-response patterns that are based on specific recurrent interactions between neurons. For example, the balance between excitatory and inhibitory currents has been identified as a central component of cortical computations. However, it remains unclear how the required synaptic connectivity can emerge in developing circuits where synapses between excitatory and inhibitory neurons are simultaneously plastic. Using theory and modeling, we propose that a wide range of cortical response properties can arise from a single plasticity paradigm that acts simultaneously at all excitatory and inhibitory connections-Hebbian learning that is stabilized by the synapse-type-specific competition for a limited supply of synaptic resources. In plastic recurrent circuits, this competition enables the formation and decorrelation of inhibition-balanced receptive fields. Networks develop an assembly structure with stronger synaptic connections between similarly tuned excitatory and inhibitory neurons and exhibit response normalization and orientation-specific center-surround suppression, reflecting the stimulus statistics during training. These results demonstrate how neurons can self-organize into functional networks and suggest an essential role for synapse-type-specific competitive learning in the development of cortical circuits.
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Affiliation(s)
- Samuel Eckmann
- Computation in Neural Circuits Group, Max Planck Institute for Brain Research, Frankfurt am Main60438, Germany
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, CambridgeCB2 1PZ, United Kingdom
| | - Edward James Young
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, CambridgeCB2 1PZ, United Kingdom
| | - Julijana Gjorgjieva
- Computation in Neural Circuits Group, Max Planck Institute for Brain Research, Frankfurt am Main60438, Germany
- School of Life Sciences, Technical University Munich, Freising85354, Germany
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5
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Franke K, Cai C, Ponder K, Fu J, Sokoloski S, Berens P, Tolias AS. Asymmetric distribution of color-opponent response types across mouse visual cortex supports superior color vision in the sky. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.06.01.543054. [PMID: 37333280 PMCID: PMC10274736 DOI: 10.1101/2023.06.01.543054] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Color is an important visual feature that informs behavior, and the retinal basis for color vision has been studied across various vertebrate species. While many studies have investigated how color information is processed in visual brain areas of primate species, we have limited understanding of how it is organized beyond the retina in other species, including most dichromatic mammals. In this study, we systematically characterized how color is represented in the primary visual cortex (V1) of mice. Using large-scale neuronal recordings and a luminance and color noise stimulus, we found that more than a third of neurons in mouse V1 are color-opponent in their receptive field center, while the receptive field surround predominantly captures luminance contrast. Furthermore, we found that color-opponency is especially pronounced in posterior V1 that encodes the sky, matching the statistics of natural scenes experienced by mice. Using unsupervised clustering, we demonstrate that the asymmetry in color representations across cortex can be explained by an uneven distribution of green-On/UV-Off color-opponent response types that are represented in the upper visual field. Finally, a simple model with natural scene-inspired parametric stimuli shows that green-On/UV-Off color-opponent response types may enhance the detection of "predatory"-like dark UV-objects in noisy daylight scenes. The results from this study highlight the relevance of color processing in the mouse visual system and contribute to our understanding of how color information is organized in the visual hierarchy across species.
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Affiliation(s)
- Katrin Franke
- Department of Ophthalmology, Byers Eye Institute, Stanford University School of Medicine, Stanford, CA, US
- Stanford Bio-X, Stanford University, Stanford, CA, US
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, US
- Department of Neuroscience & Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
| | - Chenchen Cai
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
- Graduate Training Center of Neuroscience, International Max Planck Research School, University of Tübingen, Tübingen, Germany
| | - Kayla Ponder
- Department of Neuroscience & Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
| | - Jiakun Fu
- Department of Neuroscience & Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
| | - Sacha Sokoloski
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
- Hertie Institute for AI in Brain Health, University of Tübingen, Tübingen, Germany
| | - Philipp Berens
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
- Hertie Institute for AI in Brain Health, University of Tübingen, Tübingen, Germany
| | - Andreas S Tolias
- Department of Ophthalmology, Byers Eye Institute, Stanford University School of Medicine, Stanford, CA, US
- Stanford Bio-X, Stanford University, Stanford, CA, US
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, US
- Department of Neuroscience & Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, US
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6
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Fu J, Shrinivasan S, Baroni L, Ding Z, Fahey PG, Pierzchlewicz P, Ponder K, Froebe R, Ntanavara L, Muhammad T, Willeke KF, Wang E, Ding Z, Tran DT, Papadopoulos S, Patel S, Reimer J, Ecker AS, Pitkow X, Antolik J, Sinz FH, Haefner RM, Tolias AS, Franke K. Pattern completion and disruption characterize contextual modulation in the visual cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.03.13.532473. [PMID: 36993321 PMCID: PMC10054952 DOI: 10.1101/2023.03.13.532473] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Vision is fundamentally context-dependent, with neuronal responses influenced not just by local features but also by surrounding contextual information. In the visual cortex, studies using simple grating stimuli indicate that congruent stimuli - where the center and surround share the same orientation - are more inhibitory than when orientations are orthogonal, potentially serving redundancy reduction and predictive coding. Understanding these center-surround interactions in relation to natural image statistics is challenging due to the high dimensionality of the stimulus space, yet crucial for deciphering the neuronal code of real-world sensory processing. Utilizing large-scale recordings from mouse V1, we trained convolutional neural networks (CNNs) to predict and synthesize surround patterns that either optimally suppressed or enhanced responses to center stimuli, confirmed by in vivo experiments. Contrary to the notion that congruent stimuli are suppressive, we found that surrounds that completed patterns based on natural image statistics were facilitatory, while disruptive surrounds were suppressive. Applying our CNN image synthesis method in macaque V1, we discovered that pattern completion within the near surround occurred more frequently with excitatory than with inhibitory surrounds, suggesting that our results in mice are conserved in macaques. Further, experiments and model analyses confirmed previous studies reporting the opposite effect with grating stimuli in both species. Using the MICrONS functional connectomics dataset, we observed that neurons with similar feature selectivity formed excitatory connections regardless of their receptive field overlap, aligning with the pattern completion phenomenon observed for excitatory surrounds. Finally, our empirical results emerged in a normative model of perception implementing Bayesian inference, where neuronal responses are modulated by prior knowledge of natural scene statistics. In summary, our findings identify a novel relationship between contextual information and natural scene statistics and provide evidence for a role of contextual modulation in hierarchical inference.
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7
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Papadopouli M, Smyrnakis I, Koniotakis E, Savaglio MA, Brozi C, Psilou E, Palagina G, Smirnakis SM. Brain orchestra under spontaneous conditions: Identifying communication modules from the functional architecture of area V1. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.29.582364. [PMID: 38496414 PMCID: PMC10942267 DOI: 10.1101/2024.02.29.582364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
We used two-photon imaging to record from granular and supragranular layers in mouse primary visual cortex (V1) under spontaneous conditions and applied an extension of the spike time tiling coefficient (STTC; introduced by Cutts and Eglen) to map functional connectivity architecture within and across layers. We made several observations: Approximately, 19-34% of neuronal pairs within 300 μm of each other exhibit statistically significant functional connections, compared to ~10% at distances of 1mm or more. As expected, neuronal pairs with similar tuning functions exhibit a significant, though relatively small, increase in the fraction of functional inter-neuronal correlations. In contrast, internal state as reflected by pupillary diameter or aggregate neuronal activity appears to play a much stronger role in determining inter-neuronal correlation distributions and topography. Overall, inter-neuronal correlations appear to be slightly more prominent in L4. The first-order functionally connected (i.e., direct) neighbors of neurons determine the hub structure of the V1 microcircuit. L4 exhibits a nearly flat degree of connectivity distribution, extending to higher values than seen in supragranular layers, whose distribution drops exponentially. In all layers, functional connectivity exhibits small-world characteristics and network robustness. The probability of firing of L2/3 pyramidal neurons can be predicted as a function of the aggregate activity in their first-order functionally connected partners within L4, which represent their putative input group. The functional form of this prediction conforms well to a ReLU function, reaching up to firing probability one in some neurons. Interestingly, the properties of L2/3 pyramidal neurons differ based on the size of their L4 functional connectivity group. Specifically, L2/3 neurons with small layer-4 degrees of connectivity appear to be more sensitive to the firing of their L4 functional connectivity partners, suggesting they may be more effective at transmitting synchronous activity downstream from L4. They also appear to fire largely independently from each other, compared to neurons with high layer-4 degrees of connectivity, and are less modulated by changes in pupil size and aggregate population dynamics. Information transmission is best viewed as occurring from neuronal ensembles in L4 to neuronal ensembles in L2/3. Under spontaneous conditions, we were able to identify such candidate neuronal ensembles, which exhibit high sensitivity, precision, and specificity for L4 to L2/3 information transmission. In sum, functional connectivity analysis under spontaneous activity conditions reveals a modular neuronal ensemble architecture within and across granular and supragranular layers of mouse primary visual cortex. Furthermore, modules with different degrees of connectivity appear to obey different rules of engagement and communication across the V1 columnar circuit.
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Affiliation(s)
- Maria Papadopouli
- Department of Computer Science, University of Crete, Heraklion, Greece
- Institute of Computer Science, Foundation for Research & Technology-Hellas, Heraklion, Greece
| | | | - Emmanouil Koniotakis
- Institute of Computer Science, Foundation for Research & Technology-Hellas, Heraklion, Greece
| | - Mario-Alexios Savaglio
- Department of Computer Science, University of Crete, Heraklion, Greece
- Institute of Computer Science, Foundation for Research & Technology-Hellas, Heraklion, Greece
| | - Christina Brozi
- Department of Computer Science, University of Crete, Heraklion, Greece
- Institute of Computer Science, Foundation for Research & Technology-Hellas, Heraklion, Greece
| | - Eleftheria Psilou
- Department of Computer Science, University of Crete, Heraklion, Greece
- Institute of Computer Science, Foundation for Research & Technology-Hellas, Heraklion, Greece
| | - Ganna Palagina
- Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, USA
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8
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Deveau CE, Zhou Z, LaFosse PK, Deng Y, Mirbagheri S, Steinmetz N, Histed MH. Active filtering of sequences of neural activity by recurrent circuits of sensory cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.24.581890. [PMID: 38903066 PMCID: PMC11188075 DOI: 10.1101/2024.02.24.581890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/22/2024]
Abstract
In daily life, organisms interact with a sensory world that dynamically changes from moment to moment. Recurrent neural networks can generate dynamics, but in sensory cortex any dynamic role for the dense recurrent excitatory-excitatory network has been unclear. Here we show a new role for recurrent connections in mouse visual cortex: they support powerful dynamical computations, but via filtering sequences of input instead of generating sequences. Using two-photon optogenetics, we measure responses to natural images and play them back, showing amplification when played back during the correct movie dynamic context and suppression in the incorrect context. The sequence selectivity depends on a network mechanism: inputs to groups of cells produce responses in different local neurons, which interact with later inputs to change responses. We confirm this mechanism by designing sequences of inputs that are amplified or suppressed by the network. Together, these data suggest a novel function, sequence filtering, for recurrent connections in cerebral cortex.
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Affiliation(s)
- Ciana E Deveau
- Intramural Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD USA
- NIH Graduate Partnership Program, Bethesda, MD USA
- Department of Neuroscience, Brown University, Providence RI USA
| | - Zhishang Zhou
- Intramural Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD USA
| | - Paul K LaFosse
- Intramural Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD USA
- NIH Graduate Partnership Program, Bethesda, MD USA
- Neuroscience and Cognitive Science Program, University of Maryland, College Park MD USA
| | - Yanting Deng
- Intramural Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD USA
| | - Saghar Mirbagheri
- Department of Biological Structure, University of Washington, Seattle, WA USA
| | - Nicholas Steinmetz
- Department of Biological Structure, University of Washington, Seattle, WA USA
| | - Mark H Histed
- Intramural Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD USA
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9
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Lazar A, Klein L, Klon-Lipok J, Bányai M, Orbán G, Singer W. Paying attention to natural scenes in area V1. iScience 2024; 27:108816. [PMID: 38323011 PMCID: PMC10844823 DOI: 10.1016/j.isci.2024.108816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 07/18/2023] [Accepted: 01/02/2024] [Indexed: 02/08/2024] Open
Abstract
Natural scene responses in the primary visual cortex are modulated simultaneously by attention and by contextual signals about scene statistics stored across the connectivity of the visual processing hierarchy. We hypothesized that attentional and contextual signals interact in V1 in a manner that primarily benefits the representation of natural stimuli, rich in high-order statistical structure. Recording from two macaques engaged in a spatial attention task, we found that attention enhanced the decodability of stimulus identity from population responses evoked by natural scenes, but not by synthetic stimuli lacking higher-order statistical regularities. Population analysis revealed that neuronal responses converged to a low-dimensional subspace only for natural stimuli. Critically, we determined that the attentional enhancement in stimulus decodability was captured by the natural-scene subspace, indicating an alignment between the attentional and natural stimulus variance. These results suggest that attentional and contextual signals interact in V1 in a manner optimized for natural vision.
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Affiliation(s)
- Andreea Lazar
- Ernst Strüngmann Institute, Frankfurt am Main, Germany
- Max-Planck Institute for Neuroscience, Frankfurt am Main, Germany
| | - Liane Klein
- Ernst Strüngmann Institute, Frankfurt am Main, Germany
- Max-Planck Institute for Neuroscience, Frankfurt am Main, Germany
| | - Johanna Klon-Lipok
- Ernst Strüngmann Institute, Frankfurt am Main, Germany
- Max-Planck Institute for Neuroscience, Frankfurt am Main, Germany
| | - Mihály Bányai
- HUN-REN Wigner Research Center for Physics, Budapest, Hungary
| | - Gergő Orbán
- HUN-REN Wigner Research Center for Physics, Budapest, Hungary
| | - Wolf Singer
- Ernst Strüngmann Institute, Frankfurt am Main, Germany
- Max-Planck Institute for Neuroscience, Frankfurt am Main, Germany
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10
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Purandare C, Mehta M. Mega-scale movie-fields in the mouse visuo-hippocampal network. eLife 2023; 12:RP85069. [PMID: 37910428 PMCID: PMC10619982 DOI: 10.7554/elife.85069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2023] Open
Abstract
Natural visual experience involves a continuous series of related images while the subject is immobile. How does the cortico-hippocampal circuit process a visual episode? The hippocampus is crucial for episodic memory, but most rodent single unit studies require spatial exploration or active engagement. Hence, we investigated neural responses to a silent movie (Allen Brain Observatory) in head-fixed mice without any task or locomotion demands, or rewards. Surprisingly, a third (33%, 3379/10263) of hippocampal -dentate gyrus, CA3, CA1 and subiculum- neurons showed movie-selectivity, with elevated firing in specific movie sub-segments, termed movie-fields, similar to the vast majority of thalamo-cortical (LGN, V1, AM-PM) neurons (97%, 6554/6785). Movie-tuning remained intact in immobile or spontaneously running mice. Visual neurons had >5 movie-fields per cell, but only ~2 in hippocampus. The movie-field durations in all brain regions spanned an unprecedented 1000-fold range: from 0.02s to 20s, termed mega-scale coding. Yet, the total duration of all the movie-fields of a cell was comparable across neurons and brain regions. The hippocampal responses thus showed greater continuous-sequence encoding than visual areas, as evidenced by fewer and broader movie-fields than in visual areas. Consistently, repeated presentation of the movie images in a fixed, but scrambled sequence virtually abolished hippocampal but not visual-cortical selectivity. The preference for continuous, compared to scrambled sequence was eight-fold greater in hippocampal than visual areas, further supporting episodic-sequence encoding. Movies could thus provide a unified way to probe neural mechanisms of episodic information processing and memory, even in immobile subjects, across brain regions, and species.
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Affiliation(s)
- Chinmay Purandare
- Department of Bioengineering, University of California, Los AngelesLos AngelesUnited States
- W.M. Keck Center for Neurophysics, Department of Physics and Astronomy, University of California, Los AngelesLos AngelesUnited States
- Department of Neurology, University of California, Los AngelesLos AngelesUnited States
| | - Mayank Mehta
- W.M. Keck Center for Neurophysics, Department of Physics and Astronomy, University of California, Los AngelesLos AngelesUnited States
- Department of Neurology, University of California, Los AngelesLos AngelesUnited States
- Department of Electrical and Computer Engineering, University of California, Los AngelesLos AngelesUnited States
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11
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Gehr C, Sibille J, Kremkow J. Retinal input integration in excitatory and inhibitory neurons in the mouse superior colliculus in vivo. eLife 2023; 12:RP88289. [PMID: 37682267 PMCID: PMC10491433 DOI: 10.7554/elife.88289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/09/2023] Open
Abstract
The superior colliculus (SC) is a midbrain structure that receives inputs from retinal ganglion cells (RGCs). The SC contains one of the highest densities of inhibitory neurons in the brain but whether excitatory and inhibitory SC neurons differentially integrate retinal activity in vivo is still largely unknown. We recently established a recording approach to measure the activity of RGCs simultaneously with their postsynaptic SC targets in vivo, to study how SC neurons integrate RGC activity. Here, we employ this method to investigate the functional properties that govern retinocollicular signaling in a cell type-specific manner by identifying GABAergic SC neurons using optotagging in VGAT-ChR2 mice. Our results demonstrate that both excitatory and inhibitory SC neurons receive comparably strong RGC inputs and similar wiring rules apply for RGCs innervation of both SC cell types, unlike the cell type-specific connectivity in the thalamocortical system. Moreover, retinal activity contributed more to the spiking activity of postsynaptic excitatory compared to inhibitory SC neurons. This study deepens our understanding of cell type-specific retinocollicular functional connectivity and emphasizes that the two major brain areas for visual processing, the visual cortex and the SC, differently integrate sensory afferent inputs.
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Affiliation(s)
- Carolin Gehr
- Neuroscience Research Center, Charité-Universitätsmedizin BerlinBerlinGermany
- Bernstein Center for Computational Neuroscience BerlinBerlinGermany
- Institute for Theoretical Biology, Humboldt-Universität zu BerlinBerlinGermany
- Einstein Center for Neurosciences BerlinBerlinGermany
| | - Jeremie Sibille
- Neuroscience Research Center, Charité-Universitätsmedizin BerlinBerlinGermany
- Bernstein Center for Computational Neuroscience BerlinBerlinGermany
- Institute for Theoretical Biology, Humboldt-Universität zu BerlinBerlinGermany
- Einstein Center for Neurosciences BerlinBerlinGermany
| | - Jens Kremkow
- Neuroscience Research Center, Charité-Universitätsmedizin BerlinBerlinGermany
- Bernstein Center for Computational Neuroscience BerlinBerlinGermany
- Institute for Theoretical Biology, Humboldt-Universität zu BerlinBerlinGermany
- Einstein Center for Neurosciences BerlinBerlinGermany
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12
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Wang EY, Fahey PG, Ponder K, Ding Z, Chang A, Muhammad T, Patel S, Ding Z, Tran D, Fu J, Papadopoulos S, Franke K, Ecker AS, Reimer J, Pitkow X, Sinz FH, Tolias AS. Towards a Foundation Model of the Mouse Visual Cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.21.533548. [PMID: 36993435 PMCID: PMC10055288 DOI: 10.1101/2023.03.21.533548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Understanding the brain's perception algorithm is a highly intricate problem, as the inherent complexity of sensory inputs and the brain's nonlinear processing make characterizing sensory representations difficult. Recent studies have shown that functional models-capable of predicting large-scale neuronal activity in response to arbitrary sensory input-can be powerful tools for characterizing neuronal representations by enabling high-throughput in silico experiments. However, accurately modeling responses to dynamic and ecologically relevant inputs like videos remains challenging, particularly when generalizing to new stimulus domains outside the training distribution. Inspired by recent breakthroughs in artificial intelligence, where foundation models-trained on vast quantities of data-have demonstrated remarkable capabilities and generalization, we developed a "foundation model" of the mouse visual cortex: a deep neural network trained on large amounts of neuronal responses to ecological videos from multiple visual cortical areas and mice. The model accurately predicted neuronal responses not only to natural videos but also to various new stimulus domains, such as coherent moving dots and noise patterns, underscoring its generalization abilities. The foundation model could also be adapted to new mice with minimal natural movie training data. We applied the foundation model to the MICrONS dataset: a study of the brain that integrates structure with function at unprecedented scale, containing nanometer-scale morphology, connectivity with >500,000,000 synapses, and function of >70,000 neurons within a ~1mm3 volume spanning multiple areas of the mouse visual cortex. This accurate functional model of the MICrONS data opens the possibility for a systematic characterization of the relationship between circuit structure and function. By precisely capturing the response properties of the visual cortex and generalizing to new stimulus domains and mice, foundation models can pave the way for a deeper understanding of visual computation.
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Affiliation(s)
- Eric Y Wang
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Paul G Fahey
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Kayla Ponder
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Zhuokun Ding
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Andersen Chang
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Taliah Muhammad
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Saumil Patel
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Zhiwei Ding
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Dat Tran
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Jiakun Fu
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Stelios Papadopoulos
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Katrin Franke
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Alexander S Ecker
- Institute for Computer Science, University Göttingen, Göttingen, Germany
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
| | - Jacob Reimer
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Xaq Pitkow
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Fabian H Sinz
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
- Institute for Computer Science, University Göttingen, Göttingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Germany
| | - Andreas S Tolias
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
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13
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Ding Z, Fahey PG, Papadopoulos S, Wang EY, Celii B, Papadopoulos C, Kunin AB, Chang A, Fu J, Ding Z, Patel S, Ponder K, Muhammad T, Bae JA, Bodor AL, Brittain D, Buchanan J, Bumbarger DJ, Castro MA, Cobos E, Dorkenwald S, Elabbady L, Halageri A, Jia Z, Jordan C, Kapner D, Kemnitz N, Kinn S, Lee K, Li K, Lu R, Macrina T, Mahalingam G, Mitchell E, Mondal SS, Mu S, Nehoran B, Popovych S, Schneider-Mizell CM, Silversmith W, Takeno M, Torres R, Turner NL, Wong W, Wu J, Yin W, Yu SC, Froudarakis E, Sinz F, Seung HS, Collman F, da Costa NM, Reid RC, Walker EY, Pitkow X, Reimer J, Tolias AS. Functional connectomics reveals general wiring rule in mouse visual cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.13.531369. [PMID: 36993398 PMCID: PMC10054929 DOI: 10.1101/2023.03.13.531369] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
To understand how the brain computes, it is important to unravel the relationship between circuit connectivity and function. Previous research has shown that excitatory neurons in layer 2/3 of the primary visual cortex of mice with similar response properties are more likely to form connections. However, technical challenges of combining synaptic connectivity and functional measurements have limited these studies to few, highly local connections. Utilizing the millimeter scale and nanometer resolution of the MICrONS dataset, we studied the connectivity-function relationship in excitatory neurons of the mouse visual cortex across interlaminar and interarea projections, assessing connection selectivity at the coarse axon trajectory and fine synaptic formation levels. A digital twin model of this mouse, that accurately predicted responses to arbitrary video stimuli, enabled a comprehensive characterization of the function of neurons. We found that neurons with highly correlated responses to natural videos tended to be connected with each other, not only within the same cortical area but also across multiple layers and visual areas, including feedforward and feedback connections, whereas we did not find that orientation preference predicted connectivity. The digital twin model separated each neuron's tuning into a feature component (what the neuron responds to) and a spatial component (where the neuron's receptive field is located). We show that the feature, but not the spatial component, predicted which neurons were connected at the fine synaptic scale. Together, our results demonstrate the "like-to-like" connectivity rule generalizes to multiple connection types, and the rich MICrONS dataset is suitable to further refine a mechanistic understanding of circuit structure and function.
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Affiliation(s)
- Zhuokun Ding
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Paul G Fahey
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Stelios Papadopoulos
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Eric Y Wang
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Brendan Celii
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Christos Papadopoulos
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Alexander B Kunin
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
- Department of Mathematics, Creighton University, Omaha, USA
| | - Andersen Chang
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Jiakun Fu
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Zhiwei Ding
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Saumil Patel
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Kayla Ponder
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Taliah Muhammad
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - J Alexander Bae
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Electrical and Computer Engineering Department, Princeton University, Princeton, USA
| | | | | | | | | | - Manuel A Castro
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Erick Cobos
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | | | - Akhilesh Halageri
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Zhen Jia
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Chris Jordan
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Dan Kapner
- Allen Institute for Brain Science, Seattle, USA
| | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Sam Kinn
- Allen Institute for Brain Science, Seattle, USA
| | - Kisuk Lee
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Brain & Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, USA
| | - Kai Li
- Computer Science Department, Princeton University, Princeton, USA
| | - Ran Lu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Thomas Macrina
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | | | - Eric Mitchell
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Shanka Subhra Mondal
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Electrical and Computer Engineering Department, Princeton University, Princeton, USA
| | - Shang Mu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Barak Nehoran
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - Sergiy Popovych
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | | | | | - Marc Takeno
- Allen Institute for Brain Science, Seattle, USA
| | | | - Nicholas L Turner
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Computer Science Department, Princeton University, Princeton, USA
| | - William Wong
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Jingpeng Wu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Wenjing Yin
- Allen Institute for Brain Science, Seattle, USA
| | - Szi-Chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Emmanouil Froudarakis
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
- Department of Basic Sciences, Faculty of Medicine, University of Crete, Heraklion, Greece
| | - Fabian Sinz
- Institute for Bioinformatics and Medical Informatics, University Tübingen, Tübingen, Germany
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
- Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology Hellas, Heraklion, Greece
| | - H Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | | | | | - R Clay Reid
- Allen Institute for Brain Science, Seattle, USA
| | - Edgar Y Walker
- Department of Physiology and Biophysics, University of Washington, Seattle, USA
- Computational Neuroscience Center, University of Washington, Seattle, USA
| | - Xaq Pitkow
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
- Department of Electrical and Computer Engineering, Rice University, Houston, USA
| | - Jacob Reimer
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
| | - Andreas S Tolias
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, USA
- Department of Electrical and Computer Engineering, Rice University, Houston, USA
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14
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Ding Z, Tran DT, Ponder K, Cobos E, Ding Z, Fahey PG, Wang E, Muhammad T, Fu J, Cadena SA, Papadopoulos S, Patel S, Franke K, Reimer J, Sinz FH, Ecker AS, Pitkow X, Tolias AS. Bipartite invariance in mouse primary visual cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.15.532836. [PMID: 36993218 PMCID: PMC10055119 DOI: 10.1101/2023.03.15.532836] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
A defining characteristic of intelligent systems, whether natural or artificial, is the ability to generalize and infer behaviorally relevant latent causes from high-dimensional sensory input, despite significant variations in the environment. To understand how brains achieve generalization, it is crucial to identify the features to which neurons respond selectively and invariantly. However, the high-dimensional nature of visual inputs, the non-linearity of information processing in the brain, and limited experimental time make it challenging to systematically characterize neuronal tuning and invariances, especially for natural stimuli. Here, we extended "inception loops" - a paradigm that iterates between large-scale recordings, neural predictive models, and in silico experiments followed by in vivo verification - to systematically characterize single neuron invariances in the mouse primary visual cortex. Using the predictive model we synthesized Diverse Exciting Inputs (DEIs), a set of inputs that differ substantially from each other while each driving a target neuron strongly, and verified these DEIs' efficacy in vivo. We discovered a novel bipartite invariance: one portion of the receptive field encoded phase-invariant texture-like patterns, while the other portion encoded a fixed spatial pattern. Our analysis revealed that the division between the fixed and invariant portions of the receptive fields aligns with object boundaries defined by spatial frequency differences present in highly activating natural images. These findings suggest that bipartite invariance might play a role in segmentation by detecting texture-defined object boundaries, independent of the phase of the texture. We also replicated these bipartite DEIs in the functional connectomics MICrONs data set, which opens the way towards a circuit-level mechanistic understanding of this novel type of invariance. Our study demonstrates the power of using a data-driven deep learning approach to systematically characterize neuronal invariances. By applying this method across the visual hierarchy, cell types, and sensory modalities, we can decipher how latent variables are robustly extracted from natural scenes, leading to a deeper understanding of generalization.
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Affiliation(s)
- Zhiwei Ding
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Dat T Tran
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Kayla Ponder
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Erick Cobos
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Zhuokun Ding
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Paul G Fahey
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Eric Wang
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Taliah Muhammad
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Jiakun Fu
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Santiago A Cadena
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
| | - Stelios Papadopoulos
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Saumil Patel
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Katrin Franke
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Jacob Reimer
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Fabian H Sinz
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Institute of Computer Science and Campus Institute Data Science, University of Göttingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Germany
| | - Alexander S Ecker
- Institute of Computer Science and Campus Institute Data Science, University of Göttingen, Germany
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
| | - Xaq Pitkow
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Andreas S Tolias
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
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15
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Shomali SR, Rasuli SN, Ahmadabadi MN, Shimazaki H. Uncovering hidden network architecture from spiking activities using an exact statistical input-output relation of neurons. Commun Biol 2023; 6:169. [PMID: 36792689 PMCID: PMC9932086 DOI: 10.1038/s42003-023-04511-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 01/20/2023] [Indexed: 02/17/2023] Open
Abstract
Identifying network architecture from observed neural activities is crucial in neuroscience studies. A key requirement is knowledge of the statistical input-output relation of single neurons in vivo. By utilizing an exact analytical solution of the spike-timing for leaky integrate-and-fire neurons under noisy inputs balanced near the threshold, we construct a framework that links synaptic type, strength, and spiking nonlinearity with the statistics of neuronal population activity. The framework explains structured pairwise and higher-order interactions of neurons receiving common inputs under different architectures. We compared the theoretical predictions with the activity of monkey and mouse V1 neurons and found that excitatory inputs given to pairs explained the observed sparse activity characterized by strong negative triple-wise interactions, thereby ruling out the alternative explanation by shared inhibition. Moreover, we showed that the strong interactions are a signature of excitatory rather than inhibitory inputs whenever the spontaneous rate is low. We present a guide map of neural interactions that help researchers to specify the hidden neuronal motifs underlying observed interactions found in empirical data.
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Affiliation(s)
- Safura Rashid Shomali
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, 19395-5746, Iran.
| | - Seyyed Nader Rasuli
- grid.418744.a0000 0000 8841 7951School of Physics, Institute for Research in Fundamental Sciences (IPM), Tehran, 19395-5531 Iran ,grid.411872.90000 0001 2087 2250Department of Physics, University of Guilan, Rasht, 41335-1914 Iran
| | - Majid Nili Ahmadabadi
- grid.46072.370000 0004 0612 7950Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, 14395-515 Iran
| | - Hideaki Shimazaki
- Graduate School of Informatics, Kyoto University, Kyoto, 606-8501, Japan. .,Center for Human Nature, Artificial Intelligence, and Neuroscience (CHAIN), Hokkaido University, Hokkaido, 060-0812, Japan.
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16
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Algamal M, Russ AN, Miller MR, Hou SS, Maci M, Munting LP, Zhao Q, Gerashchenko D, Bacskai BJ, Kastanenka KV. Reduced excitatory neuron activity and interneuron-type-specific deficits in a mouse model of Alzheimer's disease. Commun Biol 2022; 5:1323. [PMID: 36460716 PMCID: PMC9718858 DOI: 10.1038/s42003-022-04268-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 11/16/2022] [Indexed: 12/03/2022] Open
Abstract
Alzheimer's disease (AD) is characterized by progressive memory loss and cognitive decline. These impairments correlate with early alterations in neuronal network activity in AD patients. Disruptions in the activity of individual neurons have been reported in mouse models of amyloidosis. However, the impact of amyloid pathology on the spontaneous activity of distinct neuronal types remains unexplored in vivo. Here we use in vivo calcium imaging with multiphoton microscopy to monitor and compare the activity of excitatory and two types of inhibitory interneurons in the cortices of APP/PS1 and control mice under isoflurane anesthesia. We also determine the relationship between amyloid accumulation and the deficits in spontaneous activity in APP/PS1 mice. We show that somatostatin-expressing (SOM) interneurons are hyperactive, while parvalbumin-expressing interneurons are hypoactive in APP/PS1 mice. Only SOM interneuron hyperactivity correlated with proximity to amyloid plaque. These inhibitory deficits were accompanied by decreased excitatory neuron activity in APP/PS1 mice. Our study identifies cell-specific neuronal firing deficits in APP/PS1 mice driven by amyloid pathology. These findings highlight the importance of addressing the complexity of neuron-specific deficits to ameliorate circuit dysfunction in Alzheimer's disease.
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Affiliation(s)
- Moustafa Algamal
- Department of Neurology, MassGeneral Institute of Neurodegenerative Diseases, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Alyssa N Russ
- Department of Neurology, MassGeneral Institute of Neurodegenerative Diseases, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Morgan R Miller
- Department of Neurology, MassGeneral Institute of Neurodegenerative Diseases, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Steven S Hou
- Department of Neurology, MassGeneral Institute of Neurodegenerative Diseases, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Megi Maci
- Department of Neurology, MassGeneral Institute of Neurodegenerative Diseases, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Leon P Munting
- Department of Neurology, MassGeneral Institute of Neurodegenerative Diseases, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Qiuchen Zhao
- Department of Neurology, MassGeneral Institute of Neurodegenerative Diseases, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | | | - Brian J Bacskai
- Department of Neurology, MassGeneral Institute of Neurodegenerative Diseases, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA.
| | - Ksenia V Kastanenka
- Department of Neurology, MassGeneral Institute of Neurodegenerative Diseases, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA.
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17
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Franke K, Willeke KF, Ponder K, Galdamez M, Zhou N, Muhammad T, Patel S, Froudarakis E, Reimer J, Sinz FH, Tolias AS. State-dependent pupil dilation rapidly shifts visual feature selectivity. Nature 2022; 610:128-134. [PMID: 36171291 PMCID: PMC10635574 DOI: 10.1038/s41586-022-05270-3] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Accepted: 08/23/2022] [Indexed: 11/09/2022]
Abstract
To increase computational flexibility, the processing of sensory inputs changes with behavioural context. In the visual system, active behavioural states characterized by motor activity and pupil dilation1,2 enhance sensory responses, but typically leave the preferred stimuli of neurons unchanged2-9. Here we find that behavioural state also modulates stimulus selectivity in the mouse visual cortex in the context of coloured natural scenes. Using population imaging in behaving mice, pharmacology and deep neural network modelling, we identified a rapid shift in colour selectivity towards ultraviolet stimuli during an active behavioural state. This was exclusively caused by state-dependent pupil dilation, which resulted in a dynamic switch from rod to cone photoreceptors, thereby extending their role beyond night and day vision. The change in tuning facilitated the decoding of ethological stimuli, such as aerial predators against the twilight sky10. For decades, studies in neuroscience and cognitive science have used pupil dilation as an indirect measure of brain state. Our data suggest that, in addition, state-dependent pupil dilation itself tunes visual representations to behavioural demands by differentially recruiting rods and cones on fast timescales.
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Affiliation(s)
- Katrin Franke
- Institute for Ophthalmic Research, Tübingen University, Tübingen, Germany.
- Center for Integrative Neuroscience, Tübingen University, Tübingen, Germany.
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA.
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA.
| | - Konstantin F Willeke
- Institute for Bioinformatics and Medical Informatics, Tübingen University, Tübingen, Germany
- Department of Computer Science, Göttingen University, Göttingen, Germany
| | - Kayla Ponder
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
| | - Mario Galdamez
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
| | - Na Zhou
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
| | - Taliah Muhammad
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
| | - Saumil Patel
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
| | - Emmanouil Froudarakis
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology Hellas, Heraklion, Greece
| | - Jacob Reimer
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
| | - Fabian H Sinz
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Institute for Bioinformatics and Medical Informatics, Tübingen University, Tübingen, Germany
- Department of Computer Science, Göttingen University, Göttingen, Germany
| | - Andreas S Tolias
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
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18
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Sadeh S, Clopath C. Contribution of behavioural variability to representational drift. eLife 2022; 11:e77907. [PMID: 36040010 PMCID: PMC9481246 DOI: 10.7554/elife.77907] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 08/24/2022] [Indexed: 11/25/2022] Open
Abstract
Neuronal responses to similar stimuli change dynamically over time, raising the question of how internal representations can provide a stable substrate for neural coding. Recent work has suggested a large degree of drift in neural representations even in sensory cortices, which are believed to store stable representations of the external world. While the drift of these representations is mostly characterized in relation to external stimuli, the behavioural state of the animal (for instance, the level of arousal) is also known to strongly modulate the neural activity. We therefore asked how the variability of such modulatory mechanisms can contribute to representational changes. We analysed large-scale recording of neural activity from the Allen Brain Observatory, which was used before to document representational drift in the mouse visual cortex. We found that, within these datasets, behavioural variability significantly contributes to representational changes. This effect was broadcasted across various cortical areas in the mouse, including the primary visual cortex, higher order visual areas, and even regions not primarily linked to vision like hippocampus. Our computational modelling suggests that these results are consistent with independent modulation of neural activity by behaviour over slower timescales. Importantly, our analysis suggests that reliable but variable modulation of neural representations by behaviour can be misinterpreted as representational drift if neuronal representations are only characterized in the stimulus space and marginalized over behavioural parameters.
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Affiliation(s)
- Sadra Sadeh
- Department of Bioengineering, Imperial College LondonLondonUnited Kingdom
| | - Claudia Clopath
- Department of Bioengineering, Imperial College LondonLondonUnited Kingdom
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19
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Moroni M, Brondi M, Fellin T, Panzeri S. SmaRT2P: a software for generating and processing smart line recording trajectories for population two-photon calcium imaging. Brain Inform 2022; 9:18. [PMID: 35927517 PMCID: PMC9352634 DOI: 10.1186/s40708-022-00166-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 07/01/2022] [Indexed: 11/17/2022] Open
Abstract
Two-photon fluorescence calcium imaging allows recording the activity of large neural populations with subcellular spatial resolution, but it is typically characterized by low signal-to-noise ratio (SNR) and poor accuracy in detecting single or few action potentials when large number of neurons are imaged. We recently showed that implementing a smart line scanning approach using trajectories that optimally sample the regions of interest increases both the SNR fluorescence signals and the accuracy of single spike detection in population imaging in vivo. However, smart line scanning requires highly specialised software to design recording trajectories, interface with acquisition hardware, and efficiently process acquired data. Furthermore, smart line scanning needs optimized strategies to cope with movement artefacts and neuropil contamination. Here, we develop and validate SmaRT2P, an open-source, user-friendly and easy-to-interface Matlab-based software environment to perform optimized smart line scanning in two-photon calcium imaging experiments. SmaRT2P is designed to interface with popular acquisition software (e.g., ScanImage) and implements novel strategies to detect motion artefacts, estimate neuropil contamination, and minimize their impact on functional signals extracted from neuronal population imaging. SmaRT2P is structured in a modular way to allow flexibility in the processing pipeline, requiring minimal user intervention in parameter setting. The use of SmaRT2P for smart line scanning has the potential to facilitate the functional investigation of large neuronal populations with increased SNR and accuracy in detecting the discharge of single and few action potentials.
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Affiliation(s)
- Monica Moroni
- Neural Computation Laboratory, Center for Neuroscience and Cognitive Systems, UniTn, Istituto Italiano Di Tecnologia, 38068, Rovereto, Italy.
| | - Marco Brondi
- Optical Approaches to Brain Function Laboratory, Istituto Italiano Di Tecnologia, 16163, Genoa, Italy.,Department of Biomedical Sciences-UNIPD, Università Degli Studi Di Padova, 35121, Padua, Italy.,Padova Neuroscience Center (PNC), Università Degli Studi Di Padova, 35129, Padua, Italy
| | - Tommaso Fellin
- Optical Approaches to Brain Function Laboratory, Istituto Italiano Di Tecnologia, 16163, Genoa, Italy
| | - Stefano Panzeri
- Neural Computation Laboratory, Center for Neuroscience and Cognitive Systems, UniTn, Istituto Italiano Di Tecnologia, 38068, Rovereto, Italy. .,Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), 20251, Hamburg, Germany.
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20
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Panzeri S, Moroni M, Safaai H, Harvey CD. The structures and functions of correlations in neural population codes. Nat Rev Neurosci 2022; 23:551-567. [PMID: 35732917 DOI: 10.1038/s41583-022-00606-4] [Citation(s) in RCA: 59] [Impact Index Per Article: 29.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/19/2022] [Indexed: 12/17/2022]
Abstract
The collective activity of a population of neurons, beyond the properties of individual cells, is crucial for many brain functions. A fundamental question is how activity correlations between neurons affect how neural populations process information. Over the past 30 years, major progress has been made on how the levels and structures of correlations shape the encoding of information in population codes. Correlations influence population coding through the organization of pairwise-activity correlations with respect to the similarity of tuning of individual neurons, by their stimulus modulation and by the presence of higher-order correlations. Recent work has shown that correlations also profoundly shape other important functions performed by neural populations, including generating codes across multiple timescales and facilitating information transmission to, and readout by, downstream brain areas to guide behaviour. Here, we review this recent work and discuss how the structures of correlations can have opposite effects on the different functions of neural populations, thus creating trade-offs and constraints for the structure-function relationships of population codes. Further, we present ideas on how to combine large-scale simultaneous recordings of neural populations, computational models, analyses of behaviour, optogenetics and anatomy to unravel how the structures of correlations might be optimized to serve multiple functions.
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Affiliation(s)
- Stefano Panzeri
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany. .,Istituto Italiano di Tecnologia, Rovereto, Italy.
| | | | - Houman Safaai
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
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21
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Hu Q, Zheng Z, Sui X, Li L, Chai X, Chen Y. Spatial Attention Modulates Spike Count Correlations and Granger Causality in the Primary Visual Cortex. Front Cell Neurosci 2022; 16:838049. [PMID: 35783091 PMCID: PMC9246483 DOI: 10.3389/fncel.2022.838049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 05/23/2022] [Indexed: 11/16/2022] Open
Abstract
The influence of spatial attention on neural interactions has been revealed even in early visual information processing stages. It resolves the process of competing for sensory information about objects perceived as targets and distractors. However, the attentional modulation of the interaction between pairs of neurons with non-overlapping receptive fields (RFs) is not well known. Here, we investigated the activity of anatomically distant neurons in two behaving monkeys’ primary visual cortex (V1), when they performed a spatial attention task detecting color change. We compared attentional modulation from the perspective of spike count correlations and Granger causality among simple and complex cells. An attention-related increase in spike count correlations and a decrease in Granger causality were found. The results showed that spatial attention significantly influenced only the interactions between rather than within simple and complex cells. Furthermore, we found that the attentional modulation of neuronal interactions changed with neuronal pairs’ preferred directions differences. Thus, we found that spatial attention increased the functional communications and competing connectivities when attending to the neurons’ RFs, which impacts the interactions only between simple and complex cells. Our findings enrich the model of simple and complex cells and further understand the way that attention influences the neurons’ activities.
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Affiliation(s)
- Qiyi Hu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zhiyan Zheng
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaohong Sui
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Liming Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Xinyu Chai
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yao Chen
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
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22
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Yu Y, Stirman JN, Dorsett CR, Smith SL. Selective representations of texture and motion in mouse higher visual areas. Curr Biol 2022; 32:2810-2820.e5. [PMID: 35609609 DOI: 10.1016/j.cub.2022.04.091] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 03/22/2022] [Accepted: 04/28/2022] [Indexed: 10/18/2022]
Abstract
The mouse visual cortex contains interconnected higher visual areas, but their functional specializations are unclear. Here, we used a data-driven approach to examine the representations of complex visual stimuli by L2/3 neurons across mouse higher visual areas, measured using large-field-of-view two-photon calcium imaging. Using specialized stimuli, we found higher fidelity representations of texture in area LM, compared to area AL. Complementarily, we found higher fidelity representations of motion in area AL, compared to area LM. We also observed this segregation of information in response to naturalistic videos. Finally, we explored how receptive field models of visual cortical neurons could produce the segregated representations of texture and motion we observed. These selective representations could aid in behaviors such as visually guided navigation.
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Affiliation(s)
- Yiyi Yu
- Department of Electrical & Computer Engineering, Center for BioEngineering, Neuroscience Research Institute, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | - Jeffrey N Stirman
- Neuroscience Research Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Christopher R Dorsett
- Neuroscience Research Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Spencer L Smith
- Department of Electrical & Computer Engineering, Center for BioEngineering, Neuroscience Research Institute, University of California, Santa Barbara, Santa Barbara, CA 93106, USA.
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23
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Tangential high-density electrode insertions allow to simultaneously measure neuronal activity across an extended region of the visual field in mouse superior colliculus. J Neurosci Methods 2022; 376:109622. [PMID: 35525463 DOI: 10.1016/j.jneumeth.2022.109622] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 04/13/2022] [Accepted: 05/02/2022] [Indexed: 11/21/2022]
Abstract
BACKGROUND The superior colliculus (SC) is a midbrain structure that plays a central role in visual processing. Although we have learned a considerable amount about the function of single SC neurons, the way in which sensory information is represented and processed on the population level in awake behaving animals and across a large region of the retinotopic map is still largely unknown. Partially because the SC is anatomically located below the cortical sheet and the transverse sinus, which render the measure of neuronal activity from a large population of neurons in the SC technically difficult to perform. NEW METHOD To address this, we propose a tangential recording configuration using high-density electrode probes (Neuropixels) in mouse SC in vivo. This method permits a large number of recording sites (~200) inside the SC circuitry allowing to record from a large population of SC neurons along a vast area of retinotopic space. RESULTS This approach provides a unique opportunity to measure the activity of SC neuronal populations over up to ~2mm of SC tissue reporting for the first time the continuous receptive fields coverage of almost the entire SC retinotopy. Here we describe how to perform targeted tangential recordings along the anterior-posterior and the medio-lateral axis of the mouse SC in vivo in the upper visual layers. Furthermore, we describe how to combine this approach with optogenetic tools for cell-type identification on the population level. COMPARISON WITH EXISTING METHODS Vertical insertion has been a standard way to record visual responses in the SC. Inserting multi-shank probes vertically allows to cover a larger region of the SC but misses both the complete extent of the available retinotopy and the continuous measure allowed by the high density of recording sites on Neuropixels probes. CONCLUSION Altogether tangential insertions in the upper visual layers of the mouse SC using Neuropixels permit for the first time to access a majority of the retinotopically organized visual representation of the world at an unprecedented precision.
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24
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Zhang Y, Bu T, Zhang J, Tang S, Yu Z, Liu JK, Huang T. Decoding Pixel-Level Image Features from Two-Photon Calcium Signals of Macaque Visual Cortex. Neural Comput 2022; 34:1369-1397. [PMID: 35534008 DOI: 10.1162/neco_a_01498] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 12/20/2021] [Indexed: 11/04/2022]
Abstract
Images of visual scenes comprise essential features important for visual cognition of the brain. The complexity of visual features lies at different levels, from simple artificial patterns to natural images with different scenes. It has been a focus of using stimulus images to predict neural responses. However, it remains unclear how to extract features from neuronal responses. Here we address this question by leveraging two-photon calcium neural data recorded from the visual cortex of awake macaque monkeys. With stimuli including various categories of artificial patterns and diverse scenes of natural images, we employed a deep neural network decoder inspired by image segmentation technique. Consistent with the notation of sparse coding for natural images, a few neurons with stronger responses dominated the decoding performance, whereas decoding of ar tificial patterns needs a large number of neurons. When natural images using the model pretrained on artificial patterns are decoded, salient features of natural scenes can be extracted, as well as the conventional category information. Altogether, our results give a new perspective on studying neural encoding principles using reverse-engineering decoding strategies.
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Affiliation(s)
- Yijun Zhang
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240.,Department of Computer Science and Technology, Peking University, Peking 100871, P.R.C.
| | - Tong Bu
- Department of Computer Science and Technology, Peking University, Beijing 100871, P.R.C.
| | - Jiyuan Zhang
- Department of Computer Science and Technology, Peking University, Beijing 100871, P.R.C.
| | - Shiming Tang
- School of Life Sciences and Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, P.R.C.
| | - Zhaofei Yu
- Department of Computer Science and Technology and In stitute for Artificial Intelligence, Peking University, Beijing 100871, P.R.C.
| | - Jian K Liu
- School of Computing, University of Leeds, Leeds LS2 9JT, U.K.
| | - Tiejun Huang
- Department of Computer Science and Technology and Institute for Artificial Intelligence, Peking University, Beijing 100871, P.R.C.,Beijing Academy of Artificial Intelligence, Beijing 100190, P.R.C.
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25
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Christensen AJ, Pillow JW. Reduced neural activity but improved coding in rodent higher-order visual cortex during locomotion. Nat Commun 2022; 13:1676. [PMID: 35354804 PMCID: PMC8967903 DOI: 10.1038/s41467-022-29200-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2017] [Accepted: 03/04/2022] [Indexed: 11/19/2022] Open
Abstract
Running profoundly alters stimulus-response properties in mouse primary visual cortex (V1), but its effect in higher-order visual cortex is under-explored. Here we systematically investigate how visual responses vary with locomotive state across six visual areas and three cortical layers using a massive dataset from the Allen Brain Institute. Although previous work has shown running speed to be positively correlated with neural activity in V1, here we show that the sign of correlations between speed and neural activity varies across extra-striate cortex, and is even negative in anterior extra-striate cortex. Nevertheless, across all visual cortices, neural responses can be decoded more accurately during running than during stationary periods. We show that this effect is not attributable to changes in population activity structure, and propose that it instead arises from an increase in reliability of single-neuron responses during locomotion. The authors analyze the Allen Institute Brain Observatory Ca2+ imaging data, focusing on mouse visual cortex during locomotive and quiescent states. They find that locomotion increases neural coding fidelity, regardless of whether population activity increases or decreases in response to the population’s preferred stimuli.
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Affiliation(s)
| | - Jonathan W Pillow
- Princeton Neuroscience Institute & Department of Psychology, Princeton University, Princeton, NJ, USA
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26
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Alreja A, Nemenman I, Rozell CJ. Constrained brain volume in an efficient coding model explains the fraction of excitatory and inhibitory neurons in sensory cortices. PLoS Comput Biol 2022; 18:e1009642. [PMID: 35061666 PMCID: PMC8809590 DOI: 10.1371/journal.pcbi.1009642] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 02/02/2022] [Accepted: 11/14/2021] [Indexed: 11/18/2022] Open
Abstract
The number of neurons in mammalian cortex varies by multiple orders of magnitude across different species. In contrast, the ratio of excitatory to inhibitory neurons (E:I ratio) varies in a much smaller range, from 3:1 to 9:1 and remains roughly constant for different sensory areas within a species. Despite this structure being important for understanding the function of neural circuits, the reason for this consistency is not yet understood. While recent models of vision based on the efficient coding hypothesis show that increasing the number of both excitatory and inhibitory cells improves stimulus representation, the two cannot increase simultaneously due to constraints on brain volume. In this work, we implement an efficient coding model of vision under a constraint on the volume (using number of neurons as a surrogate) while varying the E:I ratio. We show that the performance of the model is optimal at biologically observed E:I ratios under several metrics. We argue that this happens due to trade-offs between the computational accuracy and the representation capacity for natural stimuli. Further, we make experimentally testable predictions that 1) the optimal E:I ratio should be higher for species with a higher sparsity in the neural activity and 2) the character of inhibitory synaptic distributions and firing rates should change depending on E:I ratio. Our findings, which are supported by our new preliminary analyses of publicly available data, provide the first quantitative and testable hypothesis based on optimal coding models for the distribution of excitatory and inhibitory neural types in the mammalian sensory cortices. Neurons in the brain come in two main types: excitatory and inhibitory. The interplay between them shapes neural computation. Despite brain sizes varying by several orders of magnitude across species, the ratio of excitatory and inhibitory sub-populations (E:I ratio) remains relatively constant, and we don’t know why. Simulations of theoretical models of the brain can help answer such questions, especially when experiments are prohibitive or impossible. Here we placed one such theoretical model of sensory coding (’sparse coding’ that minimizes the simultaneously active neurons) under a biophysical ‘volume’ constraint that fixes the total number of neurons available. We vary the E:I ratio in the model (which cannot be done in experiments), and reveal an optimal E:I ratio where the representation of sensory stimulus and energy consumption within the circuit are concurrently optimal. We also show that varying the population sparsity changes the optimal E:I ratio, spanning the relatively narrow ranges observed in biology. Crucially, this minimally parameterized theoretical model makes predictions about structure (recurrent connectivity) and activity (population sparsity) in neural circuits with different E:I ratios (i.e., different species), of which we verify the latter in a first-of-its-kind inter-species comparison using newly publicly available data.
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Affiliation(s)
- Arish Alreja
- Neuroscience Institute, Center for the Neural Basis of Cognition and Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Ilya Nemenman
- Department of Physics, Department of Biology and Initiative in Theory and Modeling of Living Systems, Emory University, Atlanta, Georgia, United States of America
| | - Christopher J. Rozell
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America
- * E-mail:
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27
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Abdelfattah AS, Ahuja S, Akkin T, Allu SR, Brake J, Boas DA, Buckley EM, Campbell RE, Chen AI, Cheng X, Čižmár T, Costantini I, De Vittorio M, Devor A, Doran PR, El Khatib M, Emiliani V, Fomin-Thunemann N, Fainman Y, Fernandez-Alfonso T, Ferri CGL, Gilad A, Han X, Harris A, Hillman EMC, Hochgeschwender U, Holt MG, Ji N, Kılıç K, Lake EMR, Li L, Li T, Mächler P, Miller EW, Mesquita RC, Nadella KMNS, Nägerl UV, Nasu Y, Nimmerjahn A, Ondráčková P, Pavone FS, Perez Campos C, Peterka DS, Pisano F, Pisanello F, Puppo F, Sabatini BL, Sadegh S, Sakadzic S, Shoham S, Shroff SN, Silver RA, Sims RR, Smith SL, Srinivasan VJ, Thunemann M, Tian L, Tian L, Troxler T, Valera A, Vaziri A, Vinogradov SA, Vitale F, Wang LV, Uhlířová H, Xu C, Yang C, Yang MH, Yellen G, Yizhar O, Zhao Y. Neurophotonic tools for microscopic measurements and manipulation: status report. NEUROPHOTONICS 2022; 9:013001. [PMID: 35493335 PMCID: PMC9047450 DOI: 10.1117/1.nph.9.s1.013001] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Neurophotonics was launched in 2014 coinciding with the launch of the BRAIN Initiative focused on development of technologies for advancement of neuroscience. For the last seven years, Neurophotonics' agenda has been well aligned with this focus on neurotechnologies featuring new optical methods and tools applicable to brain studies. While the BRAIN Initiative 2.0 is pivoting towards applications of these novel tools in the quest to understand the brain, this status report reviews an extensive and diverse toolkit of novel methods to explore brain function that have emerged from the BRAIN Initiative and related large-scale efforts for measurement and manipulation of brain structure and function. Here, we focus on neurophotonic tools mostly applicable to animal studies. A companion report, scheduled to appear later this year, will cover diffuse optical imaging methods applicable to noninvasive human studies. For each domain, we outline the current state-of-the-art of the respective technologies, identify the areas where innovation is needed, and provide an outlook for the future directions.
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Affiliation(s)
- Ahmed S. Abdelfattah
- Brown University, Department of Neuroscience, Providence, Rhode Island, United States
| | - Sapna Ahuja
- University of Pennsylvania, Perelman School of Medicine, Department of Biochemistry and Biophysics, Philadelphia, Pennsylvania, United States
- University of Pennsylvania, School of Arts and Sciences, Department of Chemistry, Philadelphia, Pennsylvania, United States
| | - Taner Akkin
- University of Minnesota, Department of Biomedical Engineering, Minneapolis, Minnesota, United States
| | - Srinivasa Rao Allu
- University of Pennsylvania, Perelman School of Medicine, Department of Biochemistry and Biophysics, Philadelphia, Pennsylvania, United States
- University of Pennsylvania, School of Arts and Sciences, Department of Chemistry, Philadelphia, Pennsylvania, United States
| | - Joshua Brake
- Harvey Mudd College, Department of Engineering, Claremont, California, United States
| | - David A. Boas
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Erin M. Buckley
- Georgia Institute of Technology and Emory University, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
- Emory University, Department of Pediatrics, Atlanta, Georgia, United States
| | - Robert E. Campbell
- University of Tokyo, Department of Chemistry, Tokyo, Japan
- University of Alberta, Department of Chemistry, Edmonton, Alberta, Canada
| | - Anderson I. Chen
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Xiaojun Cheng
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Tomáš Čižmár
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic
| | - Irene Costantini
- University of Florence, European Laboratory for Non-Linear Spectroscopy, Department of Biology, Florence, Italy
- National Institute of Optics, National Research Council, Rome, Italy
| | - Massimo De Vittorio
- Istituto Italiano di Tecnologia, Center for Biomolecular Nanotechnologies, Arnesano, Italy
| | - Anna Devor
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, United States
| | - Patrick R. Doran
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Mirna El Khatib
- University of Pennsylvania, Perelman School of Medicine, Department of Biochemistry and Biophysics, Philadelphia, Pennsylvania, United States
- University of Pennsylvania, School of Arts and Sciences, Department of Chemistry, Philadelphia, Pennsylvania, United States
| | | | - Natalie Fomin-Thunemann
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Yeshaiahu Fainman
- University of California San Diego, Department of Electrical and Computer Engineering, La Jolla, California, United States
| | - Tomas Fernandez-Alfonso
- University College London, Department of Neuroscience, Physiology and Pharmacology, London, United Kingdom
| | - Christopher G. L. Ferri
- University of California San Diego, Departments of Neurosciences, La Jolla, California, United States
| | - Ariel Gilad
- The Hebrew University of Jerusalem, Institute for Medical Research Israel–Canada, Department of Medical Neurobiology, Faculty of Medicine, Jerusalem, Israel
| | - Xue Han
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Andrew Harris
- Weizmann Institute of Science, Department of Brain Sciences, Rehovot, Israel
| | | | - Ute Hochgeschwender
- Central Michigan University, Department of Neuroscience, Mount Pleasant, Michigan, United States
| | - Matthew G. Holt
- University of Porto, Instituto de Investigação e Inovação em Saúde (i3S), Porto, Portugal
| | - Na Ji
- University of California Berkeley, Department of Physics, Berkeley, California, United States
| | - Kıvılcım Kılıç
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Evelyn M. R. Lake
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, Connecticut, United States
| | - Lei Li
- California Institute of Technology, Andrew and Peggy Cherng Department of Medical Engineering, Department of Electrical Engineering, Pasadena, California, United States
| | - Tianqi Li
- University of Minnesota, Department of Biomedical Engineering, Minneapolis, Minnesota, United States
| | - Philipp Mächler
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Evan W. Miller
- University of California Berkeley, Departments of Chemistry and Molecular & Cell Biology and Helen Wills Neuroscience Institute, Berkeley, California, United States
| | | | | | - U. Valentin Nägerl
- Interdisciplinary Institute for Neuroscience University of Bordeaux & CNRS, Bordeaux, France
| | - Yusuke Nasu
- University of Tokyo, Department of Chemistry, Tokyo, Japan
| | - Axel Nimmerjahn
- Salk Institute for Biological Studies, Waitt Advanced Biophotonics Center, La Jolla, California, United States
| | - Petra Ondráčková
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic
| | - Francesco S. Pavone
- National Institute of Optics, National Research Council, Rome, Italy
- University of Florence, European Laboratory for Non-Linear Spectroscopy, Department of Physics, Florence, Italy
| | - Citlali Perez Campos
- Columbia University, Zuckerman Mind Brain Behavior Institute, New York, United States
| | - Darcy S. Peterka
- Columbia University, Zuckerman Mind Brain Behavior Institute, New York, United States
| | - Filippo Pisano
- Istituto Italiano di Tecnologia, Center for Biomolecular Nanotechnologies, Arnesano, Italy
| | - Ferruccio Pisanello
- Istituto Italiano di Tecnologia, Center for Biomolecular Nanotechnologies, Arnesano, Italy
| | - Francesca Puppo
- University of California San Diego, Departments of Neurosciences, La Jolla, California, United States
| | - Bernardo L. Sabatini
- Harvard Medical School, Howard Hughes Medical Institute, Department of Neurobiology, Boston, Massachusetts, United States
| | - Sanaz Sadegh
- University of California San Diego, Departments of Neurosciences, La Jolla, California, United States
| | - Sava Sakadzic
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, United States
| | - Shy Shoham
- New York University Grossman School of Medicine, Tech4Health and Neuroscience Institutes, New York, New York, United States
| | - Sanaya N. Shroff
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - R. Angus Silver
- University College London, Department of Neuroscience, Physiology and Pharmacology, London, United Kingdom
| | - Ruth R. Sims
- Sorbonne University, INSERM, CNRS, Institut de la Vision, Paris, France
| | - Spencer L. Smith
- University of California Santa Barbara, Department of Electrical and Computer Engineering, Santa Barbara, California, United States
| | - Vivek J. Srinivasan
- New York University Langone Health, Departments of Ophthalmology and Radiology, New York, New York, United States
| | - Martin Thunemann
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Lei Tian
- Boston University, Departments of Electrical Engineering and Biomedical Engineering, Boston, Massachusetts, United States
| | - Lin Tian
- University of California Davis, Department of Biochemistry and Molecular Medicine, Davis, California, United States
| | - Thomas Troxler
- University of Pennsylvania, Perelman School of Medicine, Department of Biochemistry and Biophysics, Philadelphia, Pennsylvania, United States
- University of Pennsylvania, School of Arts and Sciences, Department of Chemistry, Philadelphia, Pennsylvania, United States
| | - Antoine Valera
- University College London, Department of Neuroscience, Physiology and Pharmacology, London, United Kingdom
| | - Alipasha Vaziri
- Rockefeller University, Laboratory of Neurotechnology and Biophysics, New York, New York, United States
- The Rockefeller University, The Kavli Neural Systems Institute, New York, New York, United States
| | - Sergei A. Vinogradov
- University of Pennsylvania, Perelman School of Medicine, Department of Biochemistry and Biophysics, Philadelphia, Pennsylvania, United States
- University of Pennsylvania, School of Arts and Sciences, Department of Chemistry, Philadelphia, Pennsylvania, United States
| | - Flavia Vitale
- Center for Neuroengineering and Therapeutics, Departments of Neurology, Bioengineering, Physical Medicine and Rehabilitation, Philadelphia, Pennsylvania, United States
| | - Lihong V. Wang
- California Institute of Technology, Andrew and Peggy Cherng Department of Medical Engineering, Department of Electrical Engineering, Pasadena, California, United States
| | - Hana Uhlířová
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic
| | - Chris Xu
- Cornell University, School of Applied and Engineering Physics, Ithaca, New York, United States
| | - Changhuei Yang
- California Institute of Technology, Departments of Electrical Engineering, Bioengineering and Medical Engineering, Pasadena, California, United States
| | - Mu-Han Yang
- University of California San Diego, Department of Electrical and Computer Engineering, La Jolla, California, United States
| | - Gary Yellen
- Harvard Medical School, Department of Neurobiology, Boston, Massachusetts, United States
| | - Ofer Yizhar
- Weizmann Institute of Science, Department of Brain Sciences, Rehovot, Israel
| | - Yongxin Zhao
- Carnegie Mellon University, Department of Biological Sciences, Pittsburgh, Pennsylvania, United States
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28
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Larisch R, Gönner L, Teichmann M, Hamker FH. Sensory coding and contrast invariance emerge from the control of plastic inhibition over emergent selectivity. PLoS Comput Biol 2021; 17:e1009566. [PMID: 34843455 PMCID: PMC8629393 DOI: 10.1371/journal.pcbi.1009566] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 10/15/2021] [Indexed: 11/18/2022] Open
Abstract
Visual stimuli are represented by a highly efficient code in the primary visual cortex, but the development of this code is still unclear. Two distinct factors control coding efficiency: Representational efficiency, which is determined by neuronal tuning diversity, and metabolic efficiency, which is influenced by neuronal gain. How these determinants of coding efficiency are shaped during development, supported by excitatory and inhibitory plasticity, is only partially understood. We investigate a fully plastic spiking network of the primary visual cortex, building on phenomenological plasticity rules. Our results suggest that inhibitory plasticity is key to the emergence of tuning diversity and accurate input encoding. We show that inhibitory feedback (random and specific) increases the metabolic efficiency by implementing a gain control mechanism. Interestingly, this led to the spontaneous emergence of contrast-invariant tuning curves. Our findings highlight that (1) interneuron plasticity is key to the development of tuning diversity and (2) that efficient sensory representations are an emergent property of the resulting network. Synaptic plasticity is crucial for the development of efficient input representation in the different sensory cortices, such as the primary visual cortex. Efficient visual representation is determined by two factors: representational efficiency, i.e. how many different input features can be represented, and metabolic efficiency, i.e. how many spikes are required to represent a specific feature. Previous research has pointed out the importance of plasticity at excitatory synapses to achieve high representational efficiency and feedback inhibition as a gain control mechanism for controlling metabolic efficiency. However, it is only partially understood how the influence of inhibitory plasticity on excitatory plasticity can lead to an efficient representation. Using a spiking neural network, we show that plasticity at feed-forward and feedback inhibitory synapses is necessary for the emergence of well-distributed neuronal selectivity to improve representational efficiency. Further, the emergent balance between excitatory and inhibitory currents improves the metabolic efficiency, and leads to contrast-invariant tuning as an inherent network property. Extending previous work, our simulation results highlight the importance of plasticity at inhibitory synapses.
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Affiliation(s)
- René Larisch
- Department of Computer Science, Artificial Intelligence, TU Chemnitz, Chemnitz, Germany
- * E-mail: (RL); (FHH)
| | - Lorenz Gönner
- Department of Computer Science, Artificial Intelligence, TU Chemnitz, Chemnitz, Germany
- Faculty of Psychology, Lifespan Developmental Neuroscience, TU Dresden, Dresden, Germany
| | - Michael Teichmann
- Department of Computer Science, Artificial Intelligence, TU Chemnitz, Chemnitz, Germany
| | - Fred H. Hamker
- Department of Computer Science, Artificial Intelligence, TU Chemnitz, Chemnitz, Germany
- Bernstein Center Computational Neuroscience, Berlin, Germany
- * E-mail: (RL); (FHH)
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29
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Lyall EH, Mossing DP, Pluta SR, Chu YW, Dudai A, Adesnik H. Synthesis of a comprehensive population code for contextual features in the awake sensory cortex. eLife 2021; 10:e62687. [PMID: 34723796 PMCID: PMC8598168 DOI: 10.7554/elife.62687] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 10/25/2021] [Indexed: 11/13/2022] Open
Abstract
How cortical circuits build representations of complex objects is poorly understood. Individual neurons must integrate broadly over space, yet simultaneously obtain sharp tuning to specific global stimulus features. Groups of neurons identifying different global features must then assemble into a population that forms a comprehensive code for these global stimulus properties. Although the logic for how single neurons summate over their spatial inputs has been well explored in anesthetized animals, how large groups of neurons compose a flexible population code of higher-order features in awake animals is not known. To address this question, we probed the integration and population coding of higher-order stimuli in the somatosensory and visual cortices of awake mice using two-photon calcium imaging across cortical layers. We developed a novel tactile stimulator that allowed the precise measurement of spatial summation even in actively whisking mice. Using this system, we found a sparse but comprehensive population code for higher-order tactile features that depends on a heterogeneous and neuron-specific logic of spatial summation beyond the receptive field. Different somatosensory cortical neurons summed specific combinations of sensory inputs supra-linearly, but integrated other inputs sub-linearly, leading to selective responses to higher-order features. Visual cortical populations employed a nearly identical scheme to generate a comprehensive population code for contextual stimuli. These results suggest that a heterogeneous logic of input-specific supra-linear summation may represent a widespread cortical mechanism for the synthesis of sparse higher-order feature codes in neural populations. This may explain how the brain exploits the thalamocortical expansion of dimensionality to encode arbitrary complex features of sensory stimuli.
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Affiliation(s)
- Evan H Lyall
- Biophysics Graduate GroupBerkeleyUnited States
- Department of Molecular and Cell BiologyBerkeleyUnited States
| | - Daniel P Mossing
- Biophysics Graduate GroupBerkeleyUnited States
- Department of Molecular and Cell BiologyBerkeleyUnited States
| | - Scott R Pluta
- Department of Molecular and Cell BiologyBerkeleyUnited States
| | - Yun Wen Chu
- Department of Molecular and Cell BiologyBerkeleyUnited States
| | - Amir Dudai
- The Edmond and Lily Safra Center for Brain Sciences and The Life Sciences Institute, The Hebrew University of JerusalemJerusalemIsrael
| | - Hillel Adesnik
- Department of Molecular and Cell BiologyBerkeleyUnited States
- The Helen Wills Neuroscience Institute, University of California, BerkeleyBerkeleyUnited States
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30
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Rikhye RV, Yildirim M, Hu M, Breton-Provencher V, Sur M. Reliable Sensory Processing in Mouse Visual Cortex through Cooperative Interactions between Somatostatin and Parvalbumin Interneurons. J Neurosci 2021; 41:8761-8778. [PMID: 34493543 PMCID: PMC8528503 DOI: 10.1523/jneurosci.3176-20.2021] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 08/16/2021] [Accepted: 08/23/2021] [Indexed: 11/21/2022] Open
Abstract
Intrinsic neuronal variability significantly limits information encoding in the primary visual cortex (V1). However, under certain conditions, neurons can respond reliably with highly precise responses to the same visual stimuli from trial to trial. This suggests that there exists intrinsic neural circuit mechanisms that dynamically modulate the intertrial variability of visual cortical neurons. Here, we sought to elucidate the role of different inhibitory interneurons (INs) in reliable coding in mouse V1. To study the interactions between somatostatin-expressing interneurons (SST-INs) and parvalbumin-expressing interneurons (PV-INs), we used a dual-color calcium imaging technique that allowed us to simultaneously monitor these two neural ensembles while awake mice, of both sexes, passively viewed natural movies. SST neurons were more active during epochs of reliable pyramidal neuron firing, whereas PV neurons were more active during epochs of unreliable firing. SST neuron activity lagged that of PV neurons, consistent with a feedback inhibitory SST→PV circuit. To dissect the role of this circuit in pyramidal neuron activity, we used temporally limited optogenetic activation and inactivation of SST and PV interneurons during periods of reliable and unreliable pyramidal cell firing. Transient firing of SST neurons increased pyramidal neuron reliability by actively suppressing PV neurons, a proposal that was supported by a rate-based model of V1 neurons. These results identify a cooperative functional role for the SST→PV circuit in modulating the reliability of pyramidal neuron activity.SIGNIFICANCE STATEMENT Cortical neurons often respond to identical sensory stimuli with large variability. However, under certain conditions, the same neurons can also respond highly reliably. The circuit mechanisms that contribute to this modulation remain unknown. Here, we used novel dual-wavelength calcium imaging and temporally selective optical perturbation to identify an inhibitory neural circuit in visual cortex that can modulate the reliability of pyramidal neurons to naturalistic visual stimuli. Our results, supported by computational models, suggest that somatostatin interneurons increase pyramidal neuron reliability by suppressing parvalbumin interneurons via the inhibitory SST→PV circuit. These findings reveal a novel role of the SST→PV circuit in modulating the fidelity of neural coding critical for visual perception.
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Affiliation(s)
- Rajeev V Rikhye
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
| | - Murat Yildirim
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
| | - Ming Hu
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
| | - Vincent Breton-Provencher
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
| | - Mriganka Sur
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
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31
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Piasini E, Soltuzu L, Muratore P, Caramellino R, Vinken K, Op de Beeck H, Balasubramanian V, Zoccolan D. Temporal stability of stimulus representation increases along rodent visual cortical hierarchies. Nat Commun 2021; 12:4448. [PMID: 34290247 PMCID: PMC8295255 DOI: 10.1038/s41467-021-24456-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Accepted: 06/14/2021] [Indexed: 11/09/2022] Open
Abstract
Cortical representations of brief, static stimuli become more invariant to identity-preserving transformations along the ventral stream. Likewise, increased invariance along the visual hierarchy should imply greater temporal persistence of temporally structured dynamic stimuli, possibly complemented by temporal broadening of neuronal receptive fields. However, such stimuli could engage adaptive and predictive processes, whose impact on neural coding dynamics is unknown. By probing the rat analog of the ventral stream with movies, we uncovered a hierarchy of temporal scales, with deeper areas encoding visual information more persistently. Furthermore, the impact of intrinsic dynamics on the stability of stimulus representations grew gradually along the hierarchy. A database of recordings from mouse showed similar trends, additionally revealing dependencies on the behavioral state. Overall, these findings show that visual representations become progressively more stable along rodent visual processing hierarchies, with an important contribution provided by intrinsic processing.
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Affiliation(s)
- Eugenio Piasini
- Computational Neuroscience Initiative, University of Pennsylvania, Philadelphia, PA, United States
| | - Liviu Soltuzu
- Visual Neuroscience Lab, International School for Advanced Studies (SISSA), Trieste, Italy
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Paolo Muratore
- Visual Neuroscience Lab, International School for Advanced Studies (SISSA), Trieste, Italy
| | - Riccardo Caramellino
- Visual Neuroscience Lab, International School for Advanced Studies (SISSA), Trieste, Italy
| | - Kasper Vinken
- Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Hans Op de Beeck
- Department of Brain and Cognition, Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Vijay Balasubramanian
- Computational Neuroscience Initiative, University of Pennsylvania, Philadelphia, PA, United States
| | - Davide Zoccolan
- Visual Neuroscience Lab, International School for Advanced Studies (SISSA), Trieste, Italy.
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32
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Amengual JL, Ben Hamed S. Revisiting Persistent Neuronal Activity During Covert Spatial Attention. Front Neural Circuits 2021; 15:679796. [PMID: 34276314 PMCID: PMC8278237 DOI: 10.3389/fncir.2021.679796] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 06/03/2021] [Indexed: 11/13/2022] Open
Abstract
Persistent activity has been observed in the prefrontal cortex (PFC), in particular during the delay periods of visual attention tasks. Classical approaches based on the average activity over multiple trials have revealed that such an activity encodes the information about the attentional instruction provided in such tasks. However, single-trial approaches have shown that activity in this area is rather sparse than persistent and highly heterogeneous not only within the trials but also between the different trials. Thus, this observation raised the question of how persistent the actually persistent attention-related prefrontal activity is and how it contributes to spatial attention. In this paper, we review recent evidence of precisely deconstructing the persistence of the neural activity in the PFC in the context of attention orienting. The inclusion of machine-learning methods for decoding the information reveals that attention orienting is a highly dynamic process, possessing intrinsic oscillatory dynamics working at multiple timescales spanning from milliseconds to minutes. Dimensionality reduction methods further show that this persistent activity dynamically incorporates multiple sources of information. This novel framework reflects a high complexity in the neural representation of the attention-related information in the PFC, and how its computational organization predicts behavior.
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Affiliation(s)
- Julian L Amengual
- Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR 5229, Université Claude Bernard Lyon I, 67 Boulevard Pinel, Bron, France
| | - Suliann Ben Hamed
- Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR 5229, Université Claude Bernard Lyon I, 67 Boulevard Pinel, Bron, France
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33
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Qiu Y, Zhao Z, Klindt D, Kautzky M, Szatko KP, Schaeffel F, Rifai K, Franke K, Busse L, Euler T. Natural environment statistics in the upper and lower visual field are reflected in mouse retinal specializations. Curr Biol 2021; 31:3233-3247.e6. [PMID: 34107304 DOI: 10.1016/j.cub.2021.05.017] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Revised: 04/06/2021] [Accepted: 05/11/2021] [Indexed: 12/29/2022]
Abstract
Pressures for survival make sensory circuits adapted to a species' natural habitat and its behavioral challenges. Thus, to advance our understanding of the visual system, it is essential to consider an animal's specific visual environment by capturing natural scenes, characterizing their statistical regularities, and using them to probe visual computations. Mice, a prominent visual system model, have salient visual specializations, being dichromatic with enhanced sensitivity to green and UV in the dorsal and ventral retina, respectively. However, the characteristics of their visual environment that likely have driven these adaptations are rarely considered. Here, we built a UV-green-sensitive camera to record footage from mouse habitats. This footage is publicly available as a resource for mouse vision research. We found chromatic contrast to greatly diverge in the upper, but not the lower, visual field. Moreover, training a convolutional autoencoder on upper, but not lower, visual field scenes was sufficient for the emergence of color-opponent filters, suggesting that this environmental difference might have driven superior chromatic opponency in the ventral mouse retina, supporting color discrimination in the upper visual field. Furthermore, the upper visual field was biased toward dark UV contrasts, paralleled by more light-offset-sensitive ganglion cells in the ventral retina. Finally, footage recorded at twilight suggests that UV promotes aerial predator detection. Our findings support that natural scene statistics shaped early visual processing in evolution.
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Affiliation(s)
- Yongrong Qiu
- Institute for Ophthalmic Research, University of Tübingen, 72076 Tübingen, Germany; Centre for Integrative Neuroscience (CIN), University of Tübingen, 72076 Tübingen, Germany; Graduate Training Centre of Neuroscience (GTC), International Max Planck Research School, University of Tübingen, 72076 Tübingen, Germany
| | - Zhijian Zhao
- Institute for Ophthalmic Research, University of Tübingen, 72076 Tübingen, Germany; Centre for Integrative Neuroscience (CIN), University of Tübingen, 72076 Tübingen, Germany
| | - David Klindt
- Institute for Ophthalmic Research, University of Tübingen, 72076 Tübingen, Germany; Centre for Integrative Neuroscience (CIN), University of Tübingen, 72076 Tübingen, Germany; Graduate Training Centre of Neuroscience (GTC), International Max Planck Research School, University of Tübingen, 72076 Tübingen, Germany
| | - Magdalena Kautzky
- Division of Neurobiology, Faculty of Biology, LMU Munich, 82152 Planegg-Martinsried, Germany; Graduate School of Systemic Neurosciences (GSN), LMU Munich, 82152 Planegg-Martinsried, Germany
| | - Klaudia P Szatko
- Institute for Ophthalmic Research, University of Tübingen, 72076 Tübingen, Germany; Centre for Integrative Neuroscience (CIN), University of Tübingen, 72076 Tübingen, Germany; Graduate Training Centre of Neuroscience (GTC), International Max Planck Research School, University of Tübingen, 72076 Tübingen, Germany; Bernstein Centre for Computational Neuroscience, 72076 Tübingen, Germany
| | - Frank Schaeffel
- Institute for Ophthalmic Research, University of Tübingen, 72076 Tübingen, Germany
| | - Katharina Rifai
- Institute for Ophthalmic Research, University of Tübingen, 72076 Tübingen, Germany; Carl Zeiss Vision International GmbH, 73430 Aalen, Germany
| | - Katrin Franke
- Institute for Ophthalmic Research, University of Tübingen, 72076 Tübingen, Germany; Centre for Integrative Neuroscience (CIN), University of Tübingen, 72076 Tübingen, Germany; Bernstein Centre for Computational Neuroscience, 72076 Tübingen, Germany
| | - Laura Busse
- Division of Neurobiology, Faculty of Biology, LMU Munich, 82152 Planegg-Martinsried, Germany; Bernstein Centre for Computational Neuroscience, 82152 Planegg-Martinsried, Germany.
| | - Thomas Euler
- Institute for Ophthalmic Research, University of Tübingen, 72076 Tübingen, Germany; Centre for Integrative Neuroscience (CIN), University of Tübingen, 72076 Tübingen, Germany; Bernstein Centre for Computational Neuroscience, 72076 Tübingen, Germany.
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34
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Sriram B, Li L, Cruz-Martín A, Ghosh A. A Sparse Probabilistic Code Underlies the Limits of Behavioral Discrimination. Cereb Cortex 2021; 30:1040-1055. [PMID: 31403676 PMCID: PMC7132908 DOI: 10.1093/cercor/bhz147] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Revised: 05/19/2019] [Accepted: 05/20/2019] [Indexed: 11/17/2022] Open
Abstract
The cortical code that underlies perception must enable subjects to perceive the world at time scales relevant for behavior. We find that mice can integrate visual stimuli very quickly (<100 ms) to reach plateau performance in an orientation discrimination task. To define features of cortical activity that underlie performance at these time scales, we measured single-unit responses in the mouse visual cortex at time scales relevant to this task. In contrast to high-contrast stimuli of longer duration, which elicit reliable activity in individual neurons, stimuli at the threshold of perception elicit extremely sparse and unreliable responses in the primary visual cortex such that the activity of individual neurons does not reliably report orientation. Integrating information across neurons, however, quickly improves performance. Using a linear decoding model, we estimate that integrating information over 50–100 neurons is sufficient to account for behavioral performance. Thus, at the limits of visual perception, the visual system integrates information encoded in the probabilistic firing of unreliable single units to generate reliable behavior.
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Affiliation(s)
- Balaji Sriram
- Division of Biology, University of California San Diego, La Jolla, CA 92093, USA.,Research and Early Development, Biogen, Cambridge, MA 02142, USA
| | - Lillian Li
- Division of Biology, University of California San Diego, La Jolla, CA 92093, USA
| | - Alberto Cruz-Martín
- Department of Biology.,Neurophotonics Center.,Department of Pharmacology and Experimental Therapeutics, Boston University, Boston, MA 02215, USA
| | - Anirvan Ghosh
- Division of Biology, University of California San Diego, La Jolla, CA 92093, USA.,Research and Early Development, Biogen, Cambridge, MA 02142, USA
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35
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Burg MF, Cadena SA, Denfield GH, Walker EY, Tolias AS, Bethge M, Ecker AS. Learning divisive normalization in primary visual cortex. PLoS Comput Biol 2021; 17:e1009028. [PMID: 34097695 PMCID: PMC8211272 DOI: 10.1371/journal.pcbi.1009028] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 06/17/2021] [Accepted: 04/30/2021] [Indexed: 11/18/2022] Open
Abstract
Divisive normalization (DN) is a prominent computational building block in the brain that has been proposed as a canonical cortical operation. Numerous experimental studies have verified its importance for capturing nonlinear neural response properties to simple, artificial stimuli, and computational studies suggest that DN is also an important component for processing natural stimuli. However, we lack quantitative models of DN that are directly informed by measurements of spiking responses in the brain and applicable to arbitrary stimuli. Here, we propose a DN model that is applicable to arbitrary input images. We test its ability to predict how neurons in macaque primary visual cortex (V1) respond to natural images, with a focus on nonlinear response properties within the classical receptive field. Our model consists of one layer of subunits followed by learned orientation-specific DN. It outperforms linear-nonlinear and wavelet-based feature representations and makes a significant step towards the performance of state-of-the-art convolutional neural network (CNN) models. Unlike deep CNNs, our compact DN model offers a direct interpretation of the nature of normalization. By inspecting the learned normalization pool of our model, we gained insights into a long-standing question about the tuning properties of DN that update the current textbook description: we found that within the receptive field oriented features were normalized preferentially by features with similar orientation rather than non-specifically as currently assumed.
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Affiliation(s)
- Max F. Burg
- Institute for Theoretical Physics and Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany
- Bernstein Center for Computational Neuroscience, Tübingen, Germany
- Institute of Computer Science and Campus Institute Data Science, University of Göttingen, Göttingen, Germany
- * E-mail:
| | - Santiago A. Cadena
- Institute for Theoretical Physics and Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany
- Bernstein Center for Computational Neuroscience, Tübingen, Germany
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, Texas, United States of America
| | - George H. Denfield
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, Texas, United States of America
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas, United States of America
| | - Edgar Y. Walker
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, Texas, United States of America
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas, United States of America
| | - Andreas S. Tolias
- Bernstein Center for Computational Neuroscience, Tübingen, Germany
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, Texas, United States of America
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas, United States of America
- Department of Electrical and Computer Engineering, Rice University, Houston, Texas, United States of America
| | - Matthias Bethge
- Institute for Theoretical Physics and Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany
- Bernstein Center for Computational Neuroscience, Tübingen, Germany
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, Texas, United States of America
| | - Alexander S. Ecker
- Institute of Computer Science and Campus Institute Data Science, University of Göttingen, Göttingen, Germany
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
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36
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Modelling and prediction of the dynamic responses of large-scale brain networks during direct electrical stimulation. Nat Biomed Eng 2021; 5:324-345. [PMID: 33526909 DOI: 10.1038/s41551-020-00666-w] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Accepted: 11/24/2020] [Indexed: 01/19/2023]
Abstract
Direct electrical stimulation can modulate the activity of brain networks for the treatment of several neurological and neuropsychiatric disorders and for restoring lost function. However, precise neuromodulation in an individual requires the accurate modelling and prediction of the effects of stimulation on the activity of their large-scale brain networks. Here, we report the development of dynamic input-output models that predict multiregional dynamics of brain networks in response to temporally varying patterns of ongoing microstimulation. In experiments with two awake rhesus macaques, we show that the activities of brain networks are modulated by changes in both stimulation amplitude and frequency, that they exhibit damping and oscillatory response dynamics, and that variabilities in prediction accuracy and in estimated response strength across brain regions can be explained by an at-rest functional connectivity measure computed without stimulation. Input-output models of brain dynamics may enable precise neuromodulation for the treatment of disease and facilitate the investigation of the functional organization of large-scale brain networks.
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37
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Exploitation of image statistics with sparse coding in the case of stereo vision. Neural Netw 2020; 135:158-176. [PMID: 33388507 DOI: 10.1016/j.neunet.2020.12.016] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Revised: 12/06/2020] [Accepted: 12/14/2020] [Indexed: 11/23/2022]
Abstract
The sparse coding algorithm has served as a model for early processing in mammalian vision. It has been assumed that the brain uses sparse coding to exploit statistical properties of the sensory stream. We hypothesize that sparse coding discovers patterns from the data set, which can be used to estimate a set of stimulus parameters by simple readout. In this study, we chose a model of stereo vision to test our hypothesis. We used the Locally Competitive Algorithm (LCA), followed by a naïve Bayes classifier, to infer stereo disparity. From the results we report three observations. First, disparity inference was successful with this naturalistic processing pipeline. Second, an expanded, highly redundant representation is required to robustly identify the input patterns. Third, the inference error can be predicted from the number of active coefficients in the LCA representation. We conclude that sparse coding can generate a suitable general representation for subsequent inference tasks.
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38
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Liu X, Zhen Z, Liu J. Hierarchical Sparse Coding of Objects in Deep Convolutional Neural Networks. Front Comput Neurosci 2020; 14:578158. [PMID: 33362499 PMCID: PMC7755594 DOI: 10.3389/fncom.2020.578158] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 11/17/2020] [Indexed: 12/04/2022] Open
Abstract
Recently, deep convolutional neural networks (DCNNs) have attained human-level performances on challenging object recognition tasks owing to their complex internal representation. However, it remains unclear how objects are represented in DCNNs with an overwhelming number of features and non-linear operations. In parallel, the same question has been extensively studied in primates' brain, and three types of coding schemes have been found: one object is coded by the entire neuronal population (distributed coding), or by one single neuron (local coding), or by a subset of neuronal population (sparse coding). Here we asked whether DCNNs adopted any of these coding schemes to represent objects. Specifically, we used the population sparseness index, which is widely-used in neurophysiological studies on primates' brain, to characterize the degree of sparseness at each layer in representative DCNNs pretrained for object categorization. We found that the sparse coding scheme was adopted at all layers of the DCNNs, and the degree of sparseness increased along the hierarchy. That is, the coding scheme shifted from distributed-like coding at lower layers to local-like coding at higher layers. Further, the degree of sparseness was positively correlated with DCNNs' performance in object categorization, suggesting that the coding scheme was related to behavioral performance. Finally, with the lesion approach, we demonstrated that both external learning experiences and built-in gating operations were necessary to construct such a hierarchical coding scheme. In sum, our study provides direct evidence that DCNNs adopted a hierarchically-evolved sparse coding scheme as the biological brain does, suggesting the possibility of an implementation-independent principle underling object recognition.
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Affiliation(s)
- Xingyu Liu
- Beijing Key Laboratory of Applied Experimental Psychology, Faculty of Psychology, Beijing Normal University, Beijing, China
| | - Zonglei Zhen
- Beijing Key Laboratory of Applied Experimental Psychology, Faculty of Psychology, Beijing Normal University, Beijing, China
| | - Jia Liu
- Department of Psychology & Tsinghua Laboratory of Brain and Intelligence, Tsinghua University, Beijing, China
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39
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Reimer ML, Bangalore L, Waxman SG, Tan AM. Core principles for the implementation of the neurodata without borders data standard. J Neurosci Methods 2020; 348:108972. [PMID: 33157146 DOI: 10.1016/j.jneumeth.2020.108972] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 08/31/2020] [Accepted: 10/10/2020] [Indexed: 10/23/2022]
Abstract
BACKGROUND The Neurodata Without Borders data standard (NWB) unifies diverse modalities of neurophysiology data in a single format. Integrating NWB with a database unleashes its full potential to promote collaboration, standardize analyses, capitalize on historical data, and ensures data integrity by maintaining process transparency. NWB database technology is the bedrock of analytical systems used by academic leaders including the Allen Institute and the International Brain Laboratory. Here we present the benefits of incorporating NWB design principles in a big data analytics application. NEW METHOD Data standards and databases are the foundation of big data analytics. To demonstrate the benefits of using these systems together, we implemented NWB in Jupyter notebooks using DataJoint to streamline database operations. RESULTS We demonstrate the utility of combining the NWB with DataJoint in a Jupyter-based electronic lab journal. We convert open-field behavioral data (using X, Y coordinates) to NWB format and process it with a DataJoint pipeline. Additional notebooks demonstrate working NWB files, data sharing, combining data from diverse sources, and retrospective analyses with data query filtering techniques. COMPARISON WITH EXISTING METHODS NWB describes how to structure and store neurophysiology data and is streamlined for research settings. In contrast to other data standards, combining NWB with DataJoint's database interface can dramatically increase data analytical capabilities. CONCLUSIONS The joint use of NWB with DataJoint transforms traditional laboratory datasets and workflows. Our Jupyter notebooks showcase the analytical and collaborative advantages of adopting big data analytics and can be tailored to other modalities by researchers interested in evaluating NWB.
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Affiliation(s)
- Marike L Reimer
- Department of Neurology and Center for Neuroscience and Regeneration Research, Yale University School of Medicine, New Haven, CT 06510, USA; Rehabilitation Research Center, Veterans Affairs Connecticut Healthcare System, West Haven, CT 06516, USA; Institute of Neuroscience, University of Oregon, Eugene, OR 97403, USA
| | - Lakshmi Bangalore
- Department of Neurology and Center for Neuroscience and Regeneration Research, Yale University School of Medicine, New Haven, CT 06510, USA; Rehabilitation Research Center, Veterans Affairs Connecticut Healthcare System, West Haven, CT 06516, USA
| | - Stephen G Waxman
- Department of Neurology and Center for Neuroscience and Regeneration Research, Yale University School of Medicine, New Haven, CT 06510, USA; Rehabilitation Research Center, Veterans Affairs Connecticut Healthcare System, West Haven, CT 06516, USA
| | - Andrew M Tan
- Department of Neurology and Center for Neuroscience and Regeneration Research, Yale University School of Medicine, New Haven, CT 06510, USA; Rehabilitation Research Center, Veterans Affairs Connecticut Healthcare System, West Haven, CT 06516, USA.
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40
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Strappini F, Wilf M, Karp O, Goldberg H, Harel M, Furman-Haran E, Golan T, Malach R. Resting-State Activity in High-Order Visual Areas as a Window into Natural Human Brain Activations. Cereb Cortex 2020; 29:3618-3635. [PMID: 30395164 DOI: 10.1093/cercor/bhy242] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Revised: 08/30/2018] [Accepted: 09/06/2018] [Indexed: 02/05/2023] Open
Abstract
A major limitation of conventional human brain research has been its basis in highly artificial laboratory experiments. Due to technical constraints, little is known about the nature of cortical activations during ecological real life. We have previously proposed the "spontaneous trait reactivation (STR)" hypothesis arguing that resting-state patterns, which emerge spontaneously in the absence of external stimulus, reflect the statistics of habitual cortical activations during real life. Therefore, these patterns can serve as a window into daily life cortical activity. A straightforward prediction of this hypothesis is that spontaneous patterns should preferentially correlate to patterns generated by naturalistic stimuli compared with artificial ones. Here we targeted high-level category-selective visual areas and tested this prediction by comparing BOLD functional connectivity patterns formed during rest to patterns formed in response to naturalistic stimuli, as well as to more artificial category-selective, dynamic stimuli. Our results revealed a significant correlation between the resting-state patterns and functional connectivity patterns generated by naturalistic stimuli. Furthermore, the correlations to naturalistic stimuli were significantly higher than those found between resting-state patterns and those generated by artificial control stimuli. These findings provide evidence of a stringent link between spontaneous patterns and the activation patterns during natural vision.
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Affiliation(s)
| | - Meytal Wilf
- Neurobiology Department, Weizmann Institute of Science, Rehovot, Israel.,Department of Clinical Neurosciences, MySpace Lab, Lausanne University Hospital, Lausanne, Switzerland
| | - Ofer Karp
- Neurobiology Department, Weizmann Institute of Science, Rehovot, Israel
| | - Hagar Goldberg
- Neurobiology Department, Weizmann Institute of Science, Rehovot, Israel
| | - Michal Harel
- Neurobiology Department, Weizmann Institute of Science, Rehovot, Israel
| | - Edna Furman-Haran
- Life Sciences Core Facilities Department, Weizmann Institute of Science, Rehovot, Israel
| | - Tal Golan
- The Edmund and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Rafael Malach
- Neurobiology Department, Weizmann Institute of Science, Rehovot, Israel
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41
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Ben Hadj Hassen S, Ben Hamed S. Functional and behavioural correlates of shared neuronal noise variability in vision and visual cognition. CURRENT OPINION IN PHYSIOLOGY 2020. [DOI: 10.1016/j.cophys.2020.07.015] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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42
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Parker PRL, Brown MA, Smear MC, Niell CM. Movement-Related Signals in Sensory Areas: Roles in Natural Behavior. Trends Neurosci 2020; 43:581-595. [PMID: 32580899 PMCID: PMC8000520 DOI: 10.1016/j.tins.2020.05.005] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 05/02/2020] [Accepted: 05/24/2020] [Indexed: 11/24/2022]
Abstract
Recent studies have demonstrated prominent and widespread movement-related signals in the brain of head-fixed mice, even in primary sensory areas. However, it is still unknown what role these signals play in sensory processing. Why are these sensory areas 'contaminated' by movement signals? During natural behavior, animals actively acquire sensory information as they move through the environment and use this information to guide ongoing actions. In this context, movement-related signals could allow sensory systems to predict self-induced sensory changes and extract additional information about the environment. In this review we summarize recent findings on the presence of movement-related signals in sensory areas and discuss how their study, in the context of natural freely moving behaviors, could advance models of sensory processing.
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Affiliation(s)
- Philip R L Parker
- Institute of Neuroscience, University of Oregon, Eugene, OR 97403, USA.
| | - Morgan A Brown
- Institute of Neuroscience, University of Oregon, Eugene, OR 97403, USA
| | - Matthew C Smear
- Institute of Neuroscience, University of Oregon, Eugene, OR 97403, USA; Department of Psychology, University of Oregon, Eugene, OR 97403, USA
| | - Cristopher M Niell
- Institute of Neuroscience, University of Oregon, Eugene, OR 97403, USA; Department of Biology, University of Oregon, Eugene, OR 97403, USA.
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43
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Distributed and retinotopically asymmetric processing of coherent motion in mouse visual cortex. Nat Commun 2020; 11:3565. [PMID: 32678087 PMCID: PMC7366664 DOI: 10.1038/s41467-020-17283-5] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Accepted: 06/23/2020] [Indexed: 12/13/2022] Open
Abstract
Perception of visual motion is important for a range of ethological behaviors in mammals. In primates, specific visual cortical regions are specialized for processing of coherent visual motion. However, whether mouse visual cortex has a similar organization remains unclear, despite powerful genetic tools available for measuring population neural activity. Here, we use widefield and 2-photon calcium imaging of transgenic mice to measure mesoscale and cellular responses to coherent motion. Imaging of primary visual cortex (V1) and higher visual areas (HVAs) during presentation of natural movies and random dot kinematograms (RDKs) reveals varied responsiveness to coherent motion, with stronger responses in dorsal stream areas compared to ventral stream areas. Moreover, there is considerable anisotropy within visual areas, such that neurons representing the lower visual field are more responsive to coherent motion. These results indicate that processing of visual motion in mouse cortex is distributed heterogeneously both across and within visual areas.
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44
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Griffiths VA, Valera AM, Lau JY, Roš H, Younts TJ, Marin B, Baragli C, Coyle D, Evans GJ, Konstantinou G, Koimtzis T, Nadella KMNS, Punde SA, Kirkby PA, Bianco IH, Silver RA. Real-time 3D movement correction for two-photon imaging in behaving animals. Nat Methods 2020; 17:741-748. [PMID: 32483335 PMCID: PMC7370269 DOI: 10.1038/s41592-020-0851-7] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Accepted: 04/28/2020] [Indexed: 11/09/2022]
Abstract
Two-photon microscopy is widely used to investigate brain function across multiple spatial scales. However, measurements of neural activity are compromised by brain movement in behaving animals. Brain motion-induced artifacts are typically corrected using post hoc processing of two-dimensional images, but this approach is slow and does not correct for axial movements. Moreover, the deleterious effects of brain movement on high-speed imaging of small regions of interest and photostimulation cannot be corrected post hoc. To address this problem, we combined random-access three-dimensional (3D) laser scanning using an acousto-optic lens and rapid closed-loop field programmable gate array processing to track 3D brain movement and correct motion artifacts in real time at up to 1 kHz. Our recordings from synapses, dendrites and large neuronal populations in behaving mice and zebrafish demonstrate real-time movement-corrected 3D two-photon imaging with submicrometer precision.
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Affiliation(s)
- Victoria A Griffiths
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Antoine M Valera
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Joanna Yn Lau
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Hana Roš
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Thomas J Younts
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Bóris Marin
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
- Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, São Bernardo do Campo, Brazil
| | - Chiara Baragli
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
- , Paris, France
| | - Diccon Coyle
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Geoffrey J Evans
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
- Department of Engineering, Sencon (UK) Ltd., Droitwich, UK
| | - George Konstantinou
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
- The Francis Crick Institute, London, UK
| | - Theo Koimtzis
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
- Optical Metrology Service, Stansted, UK
| | | | - Sameer A Punde
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Paul A Kirkby
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Isaac H Bianco
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - R Angus Silver
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK.
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45
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Abstract
Contemporary brain research seeks to understand how cognition is reducible to neural activity. Crucially, much of this effort is guided by a scientific paradigm that views neural activity as essentially driven by external stimuli. In contrast, recent perspectives argue that this paradigm is by itself inadequate and that understanding patterns of activity intrinsic to the brain is needed to explain cognition. Yet, despite this critique, the stimulus-driven paradigm still dominates-possibly because a convincing alternative has not been clear. Here, we review a series of findings suggesting such an alternative. These findings indicate that neural activity in the hippocampus occurs in one of three brain states that have radically different anatomical, physiological, representational, and behavioral correlates, together implying different functional roles in cognition. This three-state framework also indicates that neural representations in the hippocampus follow a surprising pattern of organization at the timescale of ∼1 s or longer. Lastly, beyond the hippocampus, recent breakthroughs indicate three parallel states in the cortex, suggesting shared principles and brain-wide organization of intrinsic neural activity.
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Affiliation(s)
- Kenneth Kay
- Howard Hughes Medical Institute, Kavli Institute for Fundamental Neuroscience, Department of Physiology, University of California San Francisco, San Francisco, California
| | - Loren M Frank
- Howard Hughes Medical Institute, Kavli Institute for Fundamental Neuroscience, Department of Physiology, University of California San Francisco, San Francisco, California
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46
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Yoshida T, Ohki K. Natural images are reliably represented by sparse and variable populations of neurons in visual cortex. Nat Commun 2020; 11:872. [PMID: 32054847 PMCID: PMC7018721 DOI: 10.1038/s41467-020-14645-x] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2018] [Accepted: 01/25/2020] [Indexed: 02/06/2023] Open
Abstract
Natural scenes sparsely activate neurons in the primary visual cortex (V1). However, how sparsely active neurons reliably represent complex natural images and how the information is optimally decoded from these representations have not been revealed. Using two-photon calcium imaging, we recorded visual responses to natural images from several hundred V1 neurons and reconstructed the images from neural activity in anesthetized and awake mice. A single natural image is linearly decodable from a surprisingly small number of highly responsive neurons, and the remaining neurons even degrade the decoding. Furthermore, these neurons reliably represent the image across trials, regardless of trial-to-trial response variability. Based on our results, diverse, partially overlapping receptive fields ensure sparse and reliable representation. We suggest that information is reliably represented while the corresponding neuronal patterns change across trials and collecting only the activity of highly responsive neurons is an optimal decoding strategy for the downstream neurons.
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Affiliation(s)
- Takashi Yoshida
- Department of Physiology, The University of Tokyo School of Medicine, Tokyo, Japan.
- Department of Molecular Physiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
- CREST, Japan Science and Technology Agency, Tokyo, Japan.
| | - Kenichi Ohki
- Department of Physiology, The University of Tokyo School of Medicine, Tokyo, Japan.
- Department of Molecular Physiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
- CREST, Japan Science and Technology Agency, Tokyo, Japan.
- International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo, Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.
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47
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Zhang M, Kwon SE, Ben-Johny M, O'Connor DH, Issa JB. Spectral hallmark of auditory-tactile interactions in the mouse somatosensory cortex. Commun Biol 2020; 3:64. [PMID: 32047263 PMCID: PMC7012892 DOI: 10.1038/s42003-020-0788-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Accepted: 01/22/2020] [Indexed: 11/08/2022] Open
Abstract
To synthesize a coherent representation of the external world, the brain must integrate inputs across different types of stimuli. Yet the mechanistic basis of this computation at the level of neuronal populations remains obscure. Here, we investigate tactile-auditory integration using two-photon Ca2+ imaging in the mouse primary (S1) and secondary (S2) somatosensory cortices. Pairing sound with whisker stimulation modulates tactile responses in both S1 and S2, with the most prominent modulation being robust inhibition in S2. The degree of inhibition depends on tactile stimulation frequency, with lower frequency responses the most severely attenuated. Alongside these neurons, we identify sound-selective neurons in S2 whose responses are inhibited by high tactile frequencies. These results are consistent with a hypothesized local mutually-inhibitory S2 circuit that spectrally selects tactile versus auditory inputs. Our findings enrich mechanistic understanding of multisensory integration and suggest a key role for S2 in combining auditory and tactile information.
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Affiliation(s)
- Manning Zhang
- Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, 63130, USA
| | - Sung Eun Kwon
- Solomon H. Snyder Department of Neuroscience, The Johns Hopkins University School of Medicine, Kavli Neuroscience Discovery Institute, and Brain Science Institute, Baltimore, MD, 21205, USA
- Department of Molecular, Cellular and Developmental Biology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Manu Ben-Johny
- Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
- Department of Physiology and Cellular Biophysics, Columbia University, New York, NY, 10032, USA
| | - Daniel H O'Connor
- Solomon H. Snyder Department of Neuroscience, The Johns Hopkins University School of Medicine, Kavli Neuroscience Discovery Institute, and Brain Science Institute, Baltimore, MD, 21205, USA
| | - John B Issa
- Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.
- Department of Neurobiology, Northwestern University, Evanston, IL, 60201, USA.
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48
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Inception loops discover what excites neurons most using deep predictive models. Nat Neurosci 2019; 22:2060-2065. [PMID: 31686023 DOI: 10.1038/s41593-019-0517-x] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Accepted: 09/16/2019] [Indexed: 11/09/2022]
Abstract
Finding sensory stimuli that drive neurons optimally is central to understanding information processing in the brain. However, optimizing sensory input is difficult due to the predominantly nonlinear nature of sensory processing and high dimensionality of the input. We developed 'inception loops', a closed-loop experimental paradigm combining in vivo recordings from thousands of neurons with in silico nonlinear response modeling. Our end-to-end trained, deep-learning-based model predicted thousands of neuronal responses to arbitrary, new natural input with high accuracy and was used to synthesize optimal stimuli-most exciting inputs (MEIs). For mouse primary visual cortex (V1), MEIs exhibited complex spatial features that occurred frequently in natural scenes but deviated strikingly from the common notion that Gabor-like stimuli are optimal for V1. When presented back to the same neurons in vivo, MEIs drove responses significantly better than control stimuli. Inception loops represent a widely applicable technique for dissecting the neural mechanisms of sensation.
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49
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Bányai M, Orbán G. Noise correlations and perceptual inference. Curr Opin Neurobiol 2019; 58:209-217. [PMID: 31593872 DOI: 10.1016/j.conb.2019.09.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Revised: 07/23/2019] [Accepted: 09/04/2019] [Indexed: 11/29/2022]
Affiliation(s)
- Mihály Bányai
- Computational Systems Neuroscience Lab, Wigner Research Centre for Physics, Budapest, Hungary; Center for Cognitive Computation, Central European University, Budapest, Hungary
| | - Gergő Orbán
- Computational Systems Neuroscience Lab, Wigner Research Centre for Physics, Budapest, Hungary; Center for Cognitive Computation, Central European University, Budapest, Hungary.
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50
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Abstract
In this article, we review the anatomical inputs and outputs to the mouse primary visual cortex, area V1. Our survey of data from the Allen Institute Mouse Connectivity project indicates that mouse V1 is highly interconnected with both cortical and subcortical brain areas. This pattern of innervation allows for computations that depend on the state of the animal and on behavioral goals, which contrasts with simple feedforward, hierarchical models of visual processing. Thus, to have an accurate description of the function of V1 during mouse behavior, its involvement with the rest of the brain circuitry has to be considered. Finally, it remains an open question whether the primary visual cortex of higher mammals displays the same degree of sensorimotor integration in the early visual system.
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Affiliation(s)
- Emmanouil Froudarakis
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas 77030, USA;
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Paul G Fahey
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas 77030, USA;
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Jacob Reimer
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas 77030, USA;
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Stelios M Smirnakis
- Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts 02115, USA
- Jamaica Plain VA Medical Center, Boston, Massachusetts 02130, USA
| | - Edward J Tehovnik
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas 77030, USA;
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Andreas S Tolias
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas 77030, USA;
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, Texas 77030, USA
- Department of Electrical and Computer Engineering, Rice University, Houston, Texas 77005, USA
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