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Desai NS, Zhong C, Kim R, Talmage DA, Role LW. A simple MATLAB toolbox for analyzing calcium imaging data in vitro and in vivo. J Neurosci Methods 2024; 409:110202. [PMID: 38906335 PMCID: PMC11289828 DOI: 10.1016/j.jneumeth.2024.110202] [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: 04/05/2024] [Revised: 06/06/2024] [Accepted: 06/12/2024] [Indexed: 06/23/2024]
Abstract
BACKGROUND Fluorescence imaging of calcium dynamics in neuronal populations is powerful because it offers a way of relating the activity of individual cells to the broader population of nearby cells. The method's growth across neuroscience has particularly been driven by the introduction of sophisticated mathematical techniques related to motion correction, image registration, cell detection, spike estimation, and population characterization. However, for many researchers, making good use of these techniques has been difficult because they have been devised by different workers and impose differing - and sometimes stringent - technical requirements on those who seek to use them. NEW METHOD We have built a simple toolbox of analysis routines that encompass the complete workflow for analyzing calcium imaging data. The workflow begins with preprocessing of data, includes motion correction and longitudinal image registration, detects active cells using constrained non-negative matrix factorization, and offers multiple options for estimating spike times and characterizing population activity. The routines can be navigated through a simple graphical user interface. Although written in MATLAB, a standalone version for researchers who do not have access to MATLAB is included. RESULTS We have used the toolbox on two very different preparations: spontaneously active brain slices and microendoscopic imaging from deep structures in awake behaving mice. In both cases, the toolbox offered a seamless flow from raw data all the way through to prepared graphs. CONCLUSION The field of calcium imaging has benefited from the development of numerous innovative mathematical techniques. Here we offer a simple toolbox that allows ordinary researchers to fully exploit these techniques.
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Affiliation(s)
- Niraj S Desai
- Circuits, Synapses, and Molecular Signaling Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 90892, USA.
| | - Chongbo Zhong
- Circuits, Synapses, and Molecular Signaling Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 90892, USA
| | - Ronald Kim
- Genetics of Neuronal Signaling Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 90892, USA
| | - David A Talmage
- Genetics of Neuronal Signaling Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 90892, USA
| | - Lorna W Role
- Circuits, Synapses, and Molecular Signaling Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 90892, USA.
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2
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Gavenas J, Rutishauser U, Schurger A, Maoz U. Slow ramping emerges from spontaneous fluctuations in spiking neural networks. Nat Commun 2024; 15:7285. [PMID: 39179554 PMCID: PMC11344096 DOI: 10.1038/s41467-024-51401-x] [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: 10/13/2023] [Accepted: 08/05/2024] [Indexed: 08/26/2024] Open
Abstract
The capacity to initiate actions endogenously is critical for goal-directed behavior. Spontaneous voluntary actions are typically preceded by slow-ramping activity in medial frontal cortex that begins around two seconds before movement, which may reflect spontaneous fluctuations that influence action timing. However, the mechanisms by which these slow ramping signals emerge from single-neuron and network dynamics remain poorly understood. Here, we developed a spiking neural-network model that produces spontaneous slow ramping activity in single neurons and population activity with onsets ~2 s before threshold crossings. A key prediction of our model is that neurons that ramp together have correlated firing patterns before ramping onset. We confirmed this model-derived hypothesis in a dataset of human single neuron recordings from medial frontal cortex. Our results suggest that slow ramping signals reflect bounded spontaneous fluctuations that emerge from quasi-winner-take-all dynamics in clustered networks that are temporally stabilized by slow-acting synapses.
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Affiliation(s)
- Jake Gavenas
- Institute for Interdisciplinary Brain and Behavioral Sciences, Chapman University, Orange, CA, USA.
- Schmid College of Science and Technology, Chapman University, Orange, CA, USA.
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
| | - Ueli Rutishauser
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Center for Neural Science and Medicine, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Aaron Schurger
- Institute for Interdisciplinary Brain and Behavioral Sciences, Chapman University, Orange, CA, USA
- Crean College of Health and Behavioral Sciences, Chapman University, Orange, CA, USA
- INSERM U992, Cognitive Neuroimaging Unit, NeuroSpin Center, Gif sur Yvette, 91191, France
- Commissariat à l'Energie Atomique, Direction des Sciences du Vivant, I2BM, NeuroSpin Center, Gif sur Yvette, 91191, France
| | - Uri Maoz
- Institute for Interdisciplinary Brain and Behavioral Sciences, Chapman University, Orange, CA, USA.
- Schmid College of Science and Technology, Chapman University, Orange, CA, USA.
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
- Crean College of Health and Behavioral Sciences, Chapman University, Orange, CA, USA.
- Fowler School of Engineering, Chapman University, Orange, CA, USA.
- Anderson School of Management, University of California, Los Angeles, CA, USA.
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3
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Rudelt L, González Marx D, Spitzner FP, Cramer B, Zierenberg J, Priesemann V. Signatures of hierarchical temporal processing in the mouse visual system. PLoS Comput Biol 2024; 20:e1012355. [PMID: 39173067 DOI: 10.1371/journal.pcbi.1012355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 07/23/2024] [Indexed: 08/24/2024] Open
Abstract
A core challenge for the brain is to process information across various timescales. This could be achieved by a hierarchical organization of temporal processing through intrinsic mechanisms (e.g., recurrent coupling or adaptation), but recent evidence from spike recordings of the rodent visual system seems to conflict with this hypothesis. Here, we used an optimized information-theoretic and classical autocorrelation analysis to show that information- and correlation timescales of spiking activity increase along the anatomical hierarchy of the mouse visual system under visual stimulation, while information-theoretic predictability decreases. Moreover, intrinsic timescales for spontaneous activity displayed a similar hierarchy, whereas the hierarchy of predictability was stimulus-dependent. We could reproduce these observations in a basic recurrent network model with correlated sensory input. Our findings suggest that the rodent visual system employs intrinsic mechanisms to achieve longer integration for higher cortical areas, while simultaneously reducing predictability for an efficient neural code.
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Affiliation(s)
- Lucas Rudelt
- Max-Planck-Institute for Dynamics and Self-Organization, Göttingen, Germany
- Institute for the Dynamics of Complex Systems, University of Göttingen, Göttingen, Germany
| | - Daniel González Marx
- Max-Planck-Institute for Dynamics and Self-Organization, Göttingen, Germany
- Institute for the Dynamics of Complex Systems, University of Göttingen, Göttingen, Germany
| | - F Paul Spitzner
- Max-Planck-Institute for Dynamics and Self-Organization, Göttingen, Germany
- Institute for the Dynamics of Complex Systems, University of Göttingen, Göttingen, Germany
| | - Benjamin Cramer
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
| | - Johannes Zierenberg
- Max-Planck-Institute for Dynamics and Self-Organization, Göttingen, Germany
- Institute for the Dynamics of Complex Systems, University of Göttingen, Göttingen, Germany
| | - Viola Priesemann
- Max-Planck-Institute for Dynamics and Self-Organization, Göttingen, Germany
- Institute for the Dynamics of Complex Systems, University of Göttingen, Göttingen, Germany
- Bernstein Center for Computational Neuroscience (BCCN), Göttingen, Germany
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4
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Coelho CA, Mocle AJ, Jacob AD, Ramsaran AI, Rashid AJ, Köhler S, Josselyn SA, Frankland PW. Dentate gyrus ensembles gate context-dependent neural states and memory retrieval. SCIENCE ADVANCES 2024; 10:eadn9815. [PMID: 39093976 PMCID: PMC11296340 DOI: 10.1126/sciadv.adn9815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 06/28/2024] [Indexed: 08/04/2024]
Abstract
Memories of events are linked to the contexts in which they were encoded. This contextual linking ensures enhanced access to those memories that are most relevant to the context at hand, including specific associations that were previously learned in that context. This principle, referred to as encoding specificity, predicts that context-specific neural states should bias retrieval of particular associations over others, potentially allowing for the disambiguation of retrieval cues that may have multiple associations or meanings. Using a context-odor paired associate learning paradigm in mice, here, we show that chemogenetic manipulation of dentate gyrus ensembles corresponding to specific contexts reinstates context-specific neural states in downstream CA1 and biases retrieval toward context-specific associations.
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Affiliation(s)
- Cesar A.O. Coelho
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada
| | - Andrew J. Mocle
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Physiology, University of Toronto, Toronto, ON, Canada
| | - Alex D. Jacob
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Psychology, University of Toronto, Toronto, ON, Canada
| | - Adam I. Ramsaran
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Psychology, University of Toronto, Toronto, ON, Canada
| | - Asim J. Rashid
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada
| | - Stefan Köhler
- Department of Psychology, University of Western Ontario, London, ON, Canada
| | - Sheena A. Josselyn
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Physiology, University of Toronto, Toronto, ON, Canada
- Department of Psychology, University of Toronto, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
| | - Paul W. Frankland
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Physiology, University of Toronto, Toronto, ON, Canada
- Department of Psychology, University of Toronto, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
- Child & Brain Development Program, Canadian Institute for Advanced Research, Toronto, ON, Canada
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5
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Sihn D, Chae S, Kim SP. A method to find temporal structure of neuronal coactivity patterns with across-trial correlations. J Neurosci Methods 2024; 408:110172. [PMID: 38782124 DOI: 10.1016/j.jneumeth.2024.110172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 05/08/2024] [Accepted: 05/17/2024] [Indexed: 05/25/2024]
Abstract
BACKGROUND The across-trial correlation of neurons' coactivity patterns emerges to be important for information coding, but methods for finding their temporal structures remain largely unexplored. NEW METHOD In the present study, we propose a method to find time clusters in which coactivity patterns of neurons are correlated across trials. We transform the multidimensional neural activity at each timing into a coactivity pattern of binary states, and predict the coactivity patterns at different timings. We devise a method suitable for these coactivity pattern predictions, call general event prediction. Cross-temporal prediction accuracy is then used to estimate across-trial correlations between coactivity patterns at two timings. We extract time clusters from the cross-temporal prediction accuracy by a modified k-means algorithm. RESULTS The feasibility of the proposed method is verified through simulations based on ground truth. We apply the proposed method to a calcium imaging dataset recorded from the motor cortex of mice, and demonstrate time clusters of motor cortical coactivity patterns during a motor task. COMPARISON WITH EXISTING METHODS While the existing cosine similarity method, which does not account for across-trial correlation, shows temporal structures only for contralateral neural responses, the proposed method reveals those for both contralateral and ipsilateral neural responses, demonstrating the effect of across-trial correlations. CONCLUSIONS This study introduces a novel method for measuring the temporal structure of neuronal ensemble activity.
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Affiliation(s)
- Duho Sihn
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, the Republic of Korea
| | - Soyoung Chae
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, the Republic of Korea
| | - Sung-Phil Kim
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, the Republic of Korea.
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6
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Seo S, Bharmauria V, Schütz A, Yan X, Wang H, Crawford JD. Multiunit Frontal Eye Field Activity Codes the Visuomotor Transformation, But Not Gaze Prediction or Retrospective Target Memory, in a Delayed Saccade Task. eNeuro 2024; 11:ENEURO.0413-23.2024. [PMID: 39054056 DOI: 10.1523/eneuro.0413-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 07/16/2024] [Accepted: 07/18/2024] [Indexed: 07/27/2024] Open
Abstract
Single-unit (SU) activity-action potentials isolated from one neuron-has traditionally been employed to relate neuronal activity to behavior. However, recent investigations have shown that multiunit (MU) activity-ensemble neural activity recorded within the vicinity of one microelectrode-may also contain accurate estimations of task-related neural population dynamics. Here, using an established model-fitting approach, we compared the spatial codes of SU response fields with corresponding MU response fields recorded from the frontal eye fields (FEFs) in head-unrestrained monkeys (Macaca mulatta) during a memory-guided saccade task. Overall, both SU and MU populations showed a simple visuomotor transformation: the visual response coded target-in-eye coordinates, transitioning progressively during the delay toward a future gaze-in-eye code in the saccade motor response. However, the SU population showed additional secondary codes, including a predictive gaze code in the visual response and retention of a target code in the motor response. Further, when SUs were separated into regular/fast spiking neurons, these cell types showed different spatial code progressions during the late delay period, only converging toward gaze coding during the final saccade motor response. Finally, reconstructing MU populations (by summing SU data within the same sites) failed to replicate either the SU or MU pattern. These results confirm the theoretical and practical potential of MU activity recordings as a biomarker for fundamental sensorimotor transformations (e.g., target-to-gaze coding in the oculomotor system), while also highlighting the importance of SU activity for coding more subtle (e.g., predictive/memory) aspects of sensorimotor behavior.
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Affiliation(s)
- Serah Seo
- Centre for Vision Research and Centre for Integrative and Applied Neuroscience, York University, Toronto, Ontario M3J 1P3, Canada
| | - Vishal Bharmauria
- Centre for Vision Research and Centre for Integrative and Applied Neuroscience, York University, Toronto, Ontario M3J 1P3, Canada
- Department of Neurosurgery and Brain Repair, Morsani College of Medicine, University of South Florida, Tampa, Florida 33606
| | - Adrian Schütz
- Department of Neurophysics, Philipps-Universität Marburg, 35032 Marburg, Germany
- Center for Mind, Brain, and Behavior - CMBB, Philipps-Universität Marburg, 35032 Marburg, and Justus-Liebig-Universität Giessen, Giessen, Germany
| | - Xiaogang Yan
- Centre for Vision Research and Centre for Integrative and Applied Neuroscience, York University, Toronto, Ontario M3J 1P3, Canada
| | - Hongying Wang
- Centre for Vision Research and Centre for Integrative and Applied Neuroscience, York University, Toronto, Ontario M3J 1P3, Canada
| | - J Douglas Crawford
- Centre for Vision Research and Centre for Integrative and Applied Neuroscience, York University, Toronto, Ontario M3J 1P3, Canada
- Departments of Psychology, Biology, Kinesiology & Health Sciences, York University, Toronto, Ontario M3J 1P3, Canada
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7
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Courellis HS, Minxha J, Cardenas AR, Kimmel DL, Reed CM, Valiante TA, Salzman CD, Mamelak AN, Fusi S, Rutishauser U. Abstract representations emerge in human hippocampal neurons during inference. Nature 2024; 632:841-849. [PMID: 39143207 PMCID: PMC11338822 DOI: 10.1038/s41586-024-07799-x] [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: 11/30/2023] [Accepted: 07/09/2024] [Indexed: 08/16/2024]
Abstract
Humans have the remarkable cognitive capacity to rapidly adapt to changing environments. Central to this capacity is the ability to form high-level, abstract representations that take advantage of regularities in the world to support generalization1. However, little is known about how these representations are encoded in populations of neurons, how they emerge through learning and how they relate to behaviour2,3. Here we characterized the representational geometry of populations of neurons (single units) recorded in the hippocampus, amygdala, medial frontal cortex and ventral temporal cortex of neurosurgical patients performing an inferential reasoning task. We found that only the neural representations formed in the hippocampus simultaneously encode several task variables in an abstract, or disentangled, format. This representational geometry is uniquely observed after patients learn to perform inference, and consists of disentangled directly observable and discovered latent task variables. Learning to perform inference by trial and error or through verbal instructions led to the formation of hippocampal representations with similar geometric properties. The observed relation between representational format and inference behaviour suggests that abstract and disentangled representational geometries are important for complex cognition.
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Affiliation(s)
- Hristos S Courellis
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
| | - Juri Minxha
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Araceli R Cardenas
- Krembil Research Institute and Division of Neurosurgery, University Health Network (UHN), University of Toronto, Toronto, Ontario, Canada
| | - Daniel L Kimmel
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Department of Psychiatry, Columbia University, New York, NY, USA
| | - Chrystal M Reed
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Taufik A Valiante
- Krembil Research Institute and Division of Neurosurgery, University Health Network (UHN), University of Toronto, Toronto, Ontario, Canada
| | - C Daniel Salzman
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Department of Psychiatry, Columbia University, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
- Department of Neuroscience, Columbia University, New York, NY, USA
- Kavli Institute for Brain Sciences, Columbia University, New York, NY, USA
| | - Adam N Mamelak
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Stefano Fusi
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Department of Neuroscience, Columbia University, New York, NY, USA
- Kavli Institute for Brain Sciences, Columbia University, New York, NY, USA
| | - Ueli Rutishauser
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Center for Neural Science and Medicine, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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8
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Horrocks EAB, Rodrigues FR, Saleem AB. Flexible neural population dynamics govern the speed and stability of sensory encoding in mouse visual cortex. Nat Commun 2024; 15:6415. [PMID: 39080254 PMCID: PMC11289260 DOI: 10.1038/s41467-024-50563-y] [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: 12/11/2023] [Accepted: 07/15/2024] [Indexed: 08/02/2024] Open
Abstract
Time courses of neural responses underlie real-time sensory processing and perception. How these temporal dynamics change may be fundamental to how sensory systems adapt to different perceptual demands. By simultaneously recording from hundreds of neurons in mouse primary visual cortex, we examined neural population responses to visual stimuli at sub-second timescales, during different behavioural states. We discovered that during active behavioural states characterised by locomotion, single-neurons shift from transient to sustained response modes, facilitating rapid emergence of visual stimulus tuning. Differences in single-neuron response dynamics were associated with changes in temporal dynamics of neural correlations, including faster stabilisation of stimulus-evoked changes in the structure of correlations during locomotion. Using Factor Analysis, we examined temporal dynamics of latent population responses and discovered that trajectories of population activity make more direct transitions between baseline and stimulus-encoding neural states during locomotion. This could be partly explained by dampening of oscillatory dynamics present during stationary behavioural states. Functionally, changes in temporal response dynamics collectively enabled faster, more stable and more efficient encoding of new visual information during locomotion. These findings reveal a principle of how sensory systems adapt to perceptual demands, where flexible neural population dynamics govern the speed and stability of sensory encoding.
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Affiliation(s)
- Edward A B Horrocks
- Institute of Behavioural Neuroscience, University College London, London, WC1V 0AP, UK.
| | - Fabio R Rodrigues
- Institute of Behavioural Neuroscience, University College London, London, WC1V 0AP, UK
| | - Aman B Saleem
- Institute of Behavioural Neuroscience, University College London, London, WC1V 0AP, UK.
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9
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Yan C, Mercaldo V, Jacob AD, Kramer E, Mocle A, Ramsaran AI, Tran L, Rashid AJ, Park S, Insel N, Redish AD, Frankland PW, Josselyn SA. Higher-order interactions between hippocampal CA1 neurons are disrupted in amnestic mice. Nat Neurosci 2024:10.1038/s41593-024-01713-4. [PMID: 39030342 DOI: 10.1038/s41593-024-01713-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 06/18/2024] [Indexed: 07/21/2024]
Abstract
Across systems, higher-order interactions between components govern emergent dynamics. Here we tested whether contextual threat memory retrieval in mice relies on higher-order interactions between dorsal CA1 hippocampal neurons requiring learning-induced dendritic spine plasticity. We compared population-level Ca2+ transients as wild-type mice (with intact learning-induced spine plasticity and memory) and amnestic mice (TgCRND8 mice with high levels of amyloid-β and deficits in learning-induced spine plasticity and memory) were tested for memory. Using machine-learning classifiers with different capacities to use input data with complex interactions, our findings indicate complex neuronal interactions in the memory representation of wild-type, but not amnestic, mice. Moreover, a peptide that partially restored learning-induced spine plasticity also restored the statistical complexity of the memory representation and memory behavior in Tg mice. These findings provide a previously missing bridge between levels of analysis in memory research, linking receptors, spines, higher-order neuronal dynamics and behavior.
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Affiliation(s)
- Chen Yan
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada
- DeepMind, London, UK
| | - Valentina Mercaldo
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Fundamental Neurosciences, University of Lausanne, Lausanne, Switzerland
| | - Alexander D Jacob
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
- Dept. of Psychology, University of Toronto, Toronto, Ontario, Canada
| | - Emily Kramer
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Andrew Mocle
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
- Dept. of Physiology, University of Toronto, Toronto, Ontario, Canada
| | - Adam I Ramsaran
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
- Dept. of Psychology, University of Toronto, Toronto, Ontario, Canada
| | - Lina Tran
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Asim J Rashid
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Sungmo Park
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Nathan Insel
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
- Dept. of Psychology, University of Montana, Missoula, MT, USA
- Department of Psychology, Wilfrid Laurier University, Waterloo, Ontario, Canada
| | - A David Redish
- Dept. of Neuroscience, University of Minnesota, Minneapolis, MN, USA
| | - Paul W Frankland
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada
- Dept. of Psychology, University of Toronto, Toronto, Ontario, Canada
- Dept. of Physiology, University of Toronto, Toronto, Ontario, Canada
- Child & Brain Development Program, Canadian Institute for Advanced Research (CIFAR), Toronto, Ontario, Canada
| | - Sheena A Josselyn
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada.
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada.
- Dept. of Psychology, University of Toronto, Toronto, Ontario, Canada.
- Dept. of Physiology, University of Toronto, Toronto, Ontario, Canada.
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10
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Park SB, Lur G. Repeated exposure to multiple concurrent stressors alters visual processing in the adult posterior parietal cortex. Neurobiol Stress 2024; 31:100660. [PMID: 39100726 PMCID: PMC11296072 DOI: 10.1016/j.ynstr.2024.100660] [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: 03/29/2024] [Revised: 06/25/2024] [Accepted: 06/29/2024] [Indexed: 08/06/2024] Open
Abstract
Chronic stress is well known to erode cognitive functions. Yet, our understanding of how repeated stress exposure impacts one of the fundamental bases of cognition: sensory processing, remains limited. The posterior parietal cortex (PPC) is a high order visual region, known for its role in visually guided decision making, multimodal integration, attention, and working memory. Here, we used functional measures to determine how repeated exposure to multiple concurrent stressors (RMS) affects sensory processing in the PPC in adult male mice. A longitudinal experimental design, repeatedly surveying the same population of neurons using in vivo two-photon imaging, revealed that RMS disrupts the balanced turnover of visually responsive cells in layer 2/3 of the PPC. Across the population, RMS-induced changes in visual responsiveness followed a bimodal distribution suggesting idiosyncratic stress effects. In cells that maintained their responsiveness across recording sessions, we found that stress reduced visual response magnitudes and feature selectivity. While we did not observe stress-induced elimination of excitatory synapses, noise correlation statistics indicated that RMS altered visual input to the neuronal population. The impact of RMS was restricted to visually evoked responses and was not evident in neuronal activity associated with locomotion onset. Together, our results indicate that despite no apparent synaptic reorganization, stress exposure in adulthood can disrupt sensory processing in the PPC, with the effects showing remarkable individual variation.
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Affiliation(s)
- Soo Bin Park
- Department of Neurobiology and Behavior, University of California, Irvine, CA USA, 92697
| | - Gyorgy Lur
- Department of Neurobiology and Behavior, University of California, Irvine, CA USA, 92697
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11
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Blanco Malerba S, Micheli A, Woodford M, Azeredo da Silveira R. Jointly efficient encoding and decoding in neural populations. PLoS Comput Biol 2024; 20:e1012240. [PMID: 38985828 PMCID: PMC11262701 DOI: 10.1371/journal.pcbi.1012240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 07/22/2024] [Accepted: 06/07/2024] [Indexed: 07/12/2024] Open
Abstract
The efficient coding approach proposes that neural systems represent as much sensory information as biological constraints allow. It aims at formalizing encoding as a constrained optimal process. A different approach, that aims at formalizing decoding, proposes that neural systems instantiate a generative model of the sensory world. Here, we put forth a normative framework that characterizes neural systems as jointly optimizing encoding and decoding. It takes the form of a variational autoencoder: sensory stimuli are encoded in the noisy activity of neurons to be interpreted by a flexible decoder; encoding must allow for an accurate stimulus reconstruction from neural activity. Jointly, neural activity is required to represent the statistics of latent features which are mapped by the decoder into distributions over sensory stimuli; decoding correspondingly optimizes the accuracy of the generative model. This framework yields in a family of encoding-decoding models, which result in equally accurate generative models, indexed by a measure of the stimulus-induced deviation of neural activity from the marginal distribution over neural activity. Each member of this family predicts a specific relation between properties of the sensory neurons-such as the arrangement of the tuning curve means (preferred stimuli) and widths (degrees of selectivity) in the population-as a function of the statistics of the sensory world. Our approach thus generalizes the efficient coding approach. Notably, here, the form of the constraint on the optimization derives from the requirement of an accurate generative model, while it is arbitrary in efficient coding models. Moreover, solutions do not require the knowledge of the stimulus distribution, but are learned on the basis of data samples; the constraint further acts as regularizer, allowing the model to generalize beyond the training data. Finally, we characterize the family of models we obtain through alternate measures of performance, such as the error in stimulus reconstruction. We find that a range of models admits comparable performance; in particular, a population of sensory neurons with broad tuning curves as observed experimentally yields both low reconstruction stimulus error and an accurate generative model that generalizes robustly to unseen data.
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Affiliation(s)
- Simone Blanco Malerba
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, Paris, France
- Institute for Neural Information Processing, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Aurora Micheli
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, Paris, France
| | - Michael Woodford
- Department of Economics, Columbia University, New York, New York, United States of America
| | - Rava Azeredo da Silveira
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, Paris, France
- Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland
- Faculty of Science, University of Basel, Basel, Switzerland
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12
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Combrisson E, Basanisi R, Gueguen MCM, Rheims S, Kahane P, Bastin J, Brovelli A. Neural interactions in the human frontal cortex dissociate reward and punishment learning. eLife 2024; 12:RP92938. [PMID: 38941238 PMCID: PMC11213568 DOI: 10.7554/elife.92938] [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: 06/30/2024] Open
Abstract
How human prefrontal and insular regions interact while maximizing rewards and minimizing punishments is unknown. Capitalizing on human intracranial recordings, we demonstrate that the functional specificity toward reward or punishment learning is better disentangled by interactions compared to local representations. Prefrontal and insular cortices display non-selective neural populations to rewards and punishments. Non-selective responses, however, give rise to context-specific interareal interactions. We identify a reward subsystem with redundant interactions between the orbitofrontal and ventromedial prefrontal cortices, with a driving role of the latter. In addition, we find a punishment subsystem with redundant interactions between the insular and dorsolateral cortices, with a driving role of the insula. Finally, switching between reward and punishment learning is mediated by synergistic interactions between the two subsystems. These results provide a unifying explanation of distributed cortical representations and interactions supporting reward and punishment learning.
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Affiliation(s)
- Etienne Combrisson
- Institut de Neurosciences de la Timone, Aix Marseille UniversitéMarseilleFrance
| | - Ruggero Basanisi
- Institut de Neurosciences de la Timone, Aix Marseille UniversitéMarseilleFrance
| | - Maelle CM Gueguen
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut NeurosciencesGrenobleFrance
| | - Sylvain Rheims
- Department of Functional Neurology and Epileptology, Hospices Civils de Lyon and University of LyonLyonFrance
| | - Philippe Kahane
- Univ. Grenoble Alpes, Inserm, U1216, CHU Grenoble Alpes, Grenoble Institut NeurosciencesGrenobleFrance
| | - Julien Bastin
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut NeurosciencesGrenobleFrance
| | - Andrea Brovelli
- Institut de Neurosciences de la Timone, Aix Marseille UniversitéMarseilleFrance
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13
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Panniello M, Gillon CJ, Maffulli R, Celotto M, Richards BA, Panzeri S, Kohl MM. Stimulus information guides the emergence of behavior-related signals in primary somatosensory cortex during learning. Cell Rep 2024; 43:114244. [PMID: 38796851 DOI: 10.1016/j.celrep.2024.114244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 01/16/2024] [Accepted: 05/02/2024] [Indexed: 05/29/2024] Open
Abstract
Neurons in the primary cortex carry sensory- and behavior-related information, but it remains an open question how this information emerges and intersects together during learning. Current evidence points to two possible learning-related changes: sensory information increases in the primary cortex or sensory information remains stable, but its readout efficiency in association cortices increases. We investigated this question by imaging neuronal activity in mouse primary somatosensory cortex before, during, and after learning of an object localization task. We quantified sensory- and behavior-related information and estimated how much sensory information was used to instruct perceptual choices as learning progressed. We find that sensory information increases from the start of training, while choice information is mostly present in the later stages of learning. Additionally, the readout of sensory information becomes more efficient with learning as early as in the primary sensory cortex. Together, our results highlight the importance of primary cortical neurons in perceptual learning.
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Affiliation(s)
- Mariangela Panniello
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3PT, UK; School of Psychology and Neuroscience, University of Glasgow, Glasgow G12 8QQ, UK; Optical Approaches to Brain Function Laboratory, Istituto Italiano di Tecnologia, 16163 Genova, Italy
| | - Colleen J Gillon
- Department of Biological Sciences, University of Toronto Scarborough, Toronto, ON M1C 1A4, Canada; Department of Cell & Systems Biology, University of Toronto, Toronto, ON M5S 3G5, Canada; Mila, Montréal, QC H2S 3H1, Canada
| | - Roberto Maffulli
- Neural Computation Laboratory, Center for Human Technologies, Istituto Italiano di Tecnologia, 16163 Genova, Italy
| | - Marco Celotto
- Neural Computation Laboratory, Center for Human Technologies, Istituto Italiano di Tecnologia, 16163 Genova, Italy; Institute of Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), 20251 Hamburg, Germany; Department of Pharmacy and Biotechnology, University of Bologna, 40126 Bologna, Italy
| | - Blake A Richards
- Mila, Montréal, QC H2S 3H1, Canada; School of Computer Science, McGill University, Montréal, QC H3A 2A7, Canada; Department of Neurology & Neurosurgery, McGill University, Montréal, QC H3A 1A1, Canada; Learning in Machines and Brains Program, Canadian Institute for Advanced Research, Toronto, ON M5G 1M1, Canada; Montreal Neurological Institute, Montréal, QC H3A 2B4, Canada
| | - Stefano Panzeri
- Neural Computation Laboratory, Center for Human Technologies, Istituto Italiano di Tecnologia, 16163 Genova, Italy; Institute of Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), 20251 Hamburg, Germany
| | - Michael M Kohl
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3PT, UK; School of Psychology and Neuroscience, University of Glasgow, Glasgow G12 8QQ, UK.
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14
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Koren V, Emanuel AJ, Panzeri S. Spiking networks that efficiently process dynamic sensory features explain receptor information mixing in somatosensory cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.07.597979. [PMID: 38895477 PMCID: PMC11185787 DOI: 10.1101/2024.06.07.597979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
How do biological neural systems efficiently encode, transform and propagate information between the sensory periphery and the sensory cortex about sensory features evolving at different time scales? Are these computations efficient in normative information processing terms? While previous work has suggested that biologically plausible models of of such neural information processing may be implemented efficiently within a single processing layer, how such computations extend across several processing layers is less clear. Here, we model propagation of multiple time-varying sensory features across a sensory pathway, by extending the theory of efficient coding with spikes to efficient encoding, transformation and transmission of sensory signals. These computations are optimally realized by a multilayer spiking network with feedforward networks of spiking neurons (receptor layer) and recurrent excitatory-inhibitory networks of generalized leaky integrate-and-fire neurons (recurrent layers). Our model efficiently realizes a broad class of feature transformations, including positive and negative interaction across features, through specific and biologically plausible structures of feedforward connectivity. We find that mixing of sensory features in the activity of single neurons is beneficial because it lowers the metabolic cost at the network level. We apply the model to the somatosensory pathway by constraining it with parameters measured empirically and include in its last node, analogous to the primary somatosensory cortex (S1), two types of inhibitory neurons: parvalbumin-positive neurons realizing lateral inhibition, and somatostatin-positive neurons realizing winner-take-all inhibition. By implementing a negative interaction across stimulus features, this model captures several intriguing empirical observations from the somatosensory system of the mouse, including a decrease of sustained responses from subcortical networks to S1, a non-linear effect of the knock-out of receptor neuron types on the activity in S1, and amplification of weak signals from sensory neurons across the pathway.
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Affiliation(s)
- Veronika Koren
- Institute of Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), 20251 Hamburg, Germany
| | - Alan J Emanuel
- Department of Cell Biology, Emory University School of Medicine, Atlanta, GA, 30322, USA
| | - Stefano Panzeri
- Institute of Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), 20251 Hamburg, Germany
- Istituto Italiano di Tecnologia, Genova, Italy
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15
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Koçillari L, Lorenz GM, Engel NM, Celotto M, Curreli S, Malerba SB, Engel AK, Fellin T, Panzeri S. Sampling bias corrections for accurate neural measures of redundant, unique, and synergistic information. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.04.597303. [PMID: 38895197 PMCID: PMC11185652 DOI: 10.1101/2024.06.04.597303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Shannon Information theory has long been a tool of choice to measure empirically how populations of neurons in the brain encode information about cognitive variables. Recently, Partial Information Decomposition (PID) has emerged as principled way to break down this information into components identifying not only the unique information carried by each neuron, but also whether relationships between neurons generate synergistic or redundant information. While it has been long recognized that Shannon information measures on neural activity suffer from a (mostly upward) limited sampling estimation bias, this issue has largely been ignored in the burgeoning field of PID analysis of neural activity. We used simulations to investigate the limited sampling bias of PID computed from discrete probabilities (suited to describe neural spiking activity). We found that PID suffers from a large bias that is uneven across components, with synergy by far the most biased. Using approximate analytical expansions, we found that the bias of synergy increases quadratically with the number of discrete responses of each neuron, whereas the bias of unique and redundant information increase only linearly or sub-linearly. Based on the understanding of the PID bias properties, we developed simple yet effective procedures that correct for the bias effectively, and that improve greatly the PID estimation with respect to current state-of-the-art procedures. We apply these PID bias correction procedures to datasets of 53117 pairs neurons in auditory cortex, posterior parietal cortex and hippocampus of mice performing cognitive tasks, deriving precise estimates and bounds of how synergy and redundancy vary across these brain regions.
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Affiliation(s)
- Loren Koçillari
- Institute for Neural Information Processing, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Gabriel Matías Lorenz
- Institute for Neural Information Processing, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
- Istituto Italiano di Tecnologia, Genova, Italy
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Nicola Marie Engel
- Institute for Neural Information Processing, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Marco Celotto
- Institute for Neural Information Processing, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
- Istituto Italiano di Tecnologia, Genova, Italy
| | | | - Simone Blanco Malerba
- Institute for Neural Information Processing, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Andreas K. Engel
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | | | - Stefano Panzeri
- Institute for Neural Information Processing, Center for Molecular Neurobiology, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
- Istituto Italiano di Tecnologia, Genova, Italy
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16
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Häusler S. Correlations reveal the hierarchical organization of biological networks with latent variables. Commun Biol 2024; 7:678. [PMID: 38831002 PMCID: PMC11148204 DOI: 10.1038/s42003-024-06342-y] [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: 09/19/2023] [Accepted: 05/16/2024] [Indexed: 06/05/2024] Open
Abstract
Deciphering the functional organization of large biological networks is a major challenge for current mathematical methods. A common approach is to decompose networks into largely independent functional modules, but inferring these modules and their organization from network activity is difficult, given the uncertainties and incompleteness of measurements. Typically, some parts of the overall functional organization, such as intermediate processing steps, are latent. We show that the hidden structure can be determined from the statistical moments of observable network components alone, as long as the functional relevance of the network components lies in their mean values and the mean of each latent variable maps onto a scaled expectation of a binary variable. Whether the function of biological networks permits a hierarchical modularization can be falsified by a correlation-based statistical test that we derive. We apply the test to gene regulatory networks, dendrites of pyramidal neurons, and networks of spiking neurons.
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Affiliation(s)
- Stefan Häusler
- Faculty of Biology and Bernstein Center for Computational Neuroscience, Ludwig-Maximilians-Universität München, Munich, Germany.
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17
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Pellegrino A, Stein H, Cayco-Gajic NA. Dimensionality reduction beyond neural subspaces with slice tensor component analysis. Nat Neurosci 2024; 27:1199-1210. [PMID: 38710876 DOI: 10.1038/s41593-024-01626-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 03/20/2024] [Indexed: 05/08/2024]
Abstract
Recent work has argued that large-scale neural recordings are often well described by patterns of coactivation across neurons. Yet the view that neural variability is constrained to a fixed, low-dimensional subspace may overlook higher-dimensional structure, including stereotyped neural sequences or slowly evolving latent spaces. Here we argue that task-relevant variability in neural data can also cofluctuate over trials or time, defining distinct 'covariability classes' that may co-occur within the same dataset. To demix these covariability classes, we develop sliceTCA (slice tensor component analysis), a new unsupervised dimensionality reduction method for neural data tensors. In three example datasets, including motor cortical activity during a classic reaching task in primates and recent multiregion recordings in mice, we show that sliceTCA can capture more task-relevant structure in neural data using fewer components than traditional methods. Overall, our theoretical framework extends the classic view of low-dimensional population activity by incorporating additional classes of latent variables capturing higher-dimensional structure.
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Affiliation(s)
- Arthur Pellegrino
- Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Département D'Etudes Cognitives, Ecole Normale Supérieure, PSL University, Paris, France.
- Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, UK.
| | - Heike Stein
- Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Département D'Etudes Cognitives, Ecole Normale Supérieure, PSL University, Paris, France
| | - N Alex Cayco-Gajic
- Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Département D'Etudes Cognitives, Ecole Normale Supérieure, PSL University, Paris, France.
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18
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Painchaud V, Desrosiers P, Doyon N. The Determining Role of Covariances in Large Networks of Stochastic Neurons. Neural Comput 2024; 36:1121-1162. [PMID: 38657971 DOI: 10.1162/neco_a_01656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 01/02/2024] [Indexed: 04/26/2024]
Abstract
Biological neural networks are notoriously hard to model due to their stochastic behavior and high dimensionality. We tackle this problem by constructing a dynamical model of both the expectations and covariances of the fractions of active and refractory neurons in the network's populations. We do so by describing the evolution of the states of individual neurons with a continuous-time Markov chain, from which we formally derive a low-dimensional dynamical system. This is done by solving a moment closure problem in a way that is compatible with the nonlinearity and boundedness of the activation function. Our dynamical system captures the behavior of the high-dimensional stochastic model even in cases where the mean-field approximation fails to do so. Taking into account the second-order moments modifies the solutions that would be obtained with the mean-field approximation and can lead to the appearance or disappearance of fixed points and limit cycles. We moreover perform numerical experiments where the mean-field approximation leads to periodically oscillating solutions, while the solutions of the second-order model can be interpreted as an average taken over many realizations of the stochastic model. Altogether, our results highlight the importance of including higher moments when studying stochastic networks and deepen our understanding of correlated neuronal activity.
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Affiliation(s)
- Vincent Painchaud
- Department of Mathematics and Statistics, McGill University, Montreal, Québec H3A 0B6, Canada
| | - Patrick Desrosiers
- Department of Physics, Engineering Physics, and Optics, Université Laval, Quebec City, Québec G1V 0A6, Canada
- CERVO Brain Research Center, Quebec City, Québec G1E 1T2, Canada
- Centre interdisciplinaire en modélisation mathématique de l'Université Laval, Quebec City, Québec G1V 0A6, Canada
| | - Nicolas Doyon
- Départment of Mathematics and Statistics, Université Laval, Quebec City, Québec G1V 0A6, Canada
- CERVO Brain Research Center, Quebec City, Québec G1E 1T2, Canada
- Centre interdisciplinaire en modélisation mathématique de l'Université Laval, Quebec City, Québec G1V 0A6, Canada
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19
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Gelens F, Äijälä J, Roberts L, Komatsu M, Uran C, Jensen MA, Miller KJ, Ince RAA, Garagnani M, Vinck M, Canales-Johnson A. Distributed representations of prediction error signals across the cortical hierarchy are synergistic. Nat Commun 2024; 15:3941. [PMID: 38729937 PMCID: PMC11087548 DOI: 10.1038/s41467-024-48329-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 04/26/2024] [Indexed: 05/12/2024] Open
Abstract
A relevant question concerning inter-areal communication in the cortex is whether these interactions are synergistic. Synergy refers to the complementary effect of multiple brain signals conveying more information than the sum of each isolated signal. Redundancy, on the other hand, refers to the common information shared between brain signals. Here, we dissociated cortical interactions encoding complementary information (synergy) from those sharing common information (redundancy) during prediction error (PE) processing. We analyzed auditory and frontal electrocorticography (ECoG) signals in five common awake marmosets performing two distinct auditory oddball tasks and investigated to what extent event-related potentials (ERP) and broadband (BB) dynamics encoded synergistic and redundant information about PE processing. The information conveyed by ERPs and BB signals was synergistic even at lower stages of the hierarchy in the auditory cortex and between auditory and frontal regions. Using a brain-constrained neural network, we simulated the synergy and redundancy observed in the experimental results and demonstrated that the emergence of synergy between auditory and frontal regions requires the presence of strong, long-distance, feedback, and feedforward connections. These results indicate that distributed representations of PE signals across the cortical hierarchy can be highly synergistic.
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Affiliation(s)
- Frank Gelens
- Department of Psychology, University of Amsterdam, Nieuwe Achtergracht 129-B, 1018 WT, Amsterdam, The Netherlands
- Department of Psychology, University of Cambridge, CB2 3EB, Cambridge, UK
| | - Juho Äijälä
- Department of Psychology, University of Cambridge, CB2 3EB, Cambridge, UK
| | - Louis Roberts
- Department of Psychology, University of Cambridge, CB2 3EB, Cambridge, UK
- Department of Computing, Goldsmiths, University of London, SE14 6NW, London, UK
| | - Misako Komatsu
- Laboratory for Haptic Perception and Cognitive Physiology, RIKEN Brain Science Institute, Saitama, 351-0198, Japan
| | - Cem Uran
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528, Frankfurt am Main, Germany
- Donders Centre for Neuroscience, Department of Neuroinformatics, Radboud University Nijmegen, 6525, Nijmegen, The Netherlands
| | - Michael A Jensen
- Department of Neurosurgery, Mayo Clinic, Rochester, MN, 55905, USA
| | - Kai J Miller
- Department of Neurosurgery, Mayo Clinic, Rochester, MN, 55905, USA
| | - Robin A A Ince
- School of Psychology and Neuroscience, University of Glasgow, Glasgow, G12 8QB, Scotland, UK
| | - Max Garagnani
- Department of Computing, Goldsmiths, University of London, SE14 6NW, London, UK
- Brain Language Lab, Freie Universität Berlin, 14195, Berlin, Germany
| | - Martin Vinck
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528, Frankfurt am Main, Germany.
- Donders Centre for Neuroscience, Department of Neuroinformatics, Radboud University Nijmegen, 6525, Nijmegen, The Netherlands.
| | - Andres Canales-Johnson
- Department of Psychology, University of Cambridge, CB2 3EB, Cambridge, UK.
- Neuropsychology and Cognitive Neurosciences Research Center, Faculty of Health Sciences, Universidad Católica del Maule, 3460000, Talca, Chile.
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20
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Daume J, Kamiński J, Schjetnan AGP, Salimpour Y, Khan U, Kyzar M, Reed CM, Anderson WS, Valiante TA, Mamelak AN, Rutishauser U. Control of working memory by phase-amplitude coupling of human hippocampal neurons. Nature 2024; 629:393-401. [PMID: 38632400 PMCID: PMC11078732 DOI: 10.1038/s41586-024-07309-z] [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: 05/03/2023] [Accepted: 03/13/2024] [Indexed: 04/19/2024]
Abstract
Retaining information in working memory is a demanding process that relies on cognitive control to protect memoranda-specific persistent activity from interference1,2. However, how cognitive control regulates working memory storage is unclear. Here we show that interactions of frontal control and hippocampal persistent activity are coordinated by theta-gamma phase-amplitude coupling (TG-PAC). We recorded single neurons in the human medial temporal and frontal lobe while patients maintained multiple items in their working memory. In the hippocampus, TG-PAC was indicative of working memory load and quality. We identified cells that selectively spiked during nonlinear interactions of theta phase and gamma amplitude. The spike timing of these PAC neurons was coordinated with frontal theta activity when cognitive control demand was high. By introducing noise correlations with persistently active neurons in the hippocampus, PAC neurons shaped the geometry of the population code. This led to higher-fidelity representations of working memory content that were associated with improved behaviour. Our results support a multicomponent architecture of working memory1,2, with frontal control managing maintenance of working memory content in storage-related areas3-5. Within this framework, hippocampal TG-PAC integrates cognitive control and working memory storage across brain areas, thereby suggesting a potential mechanism for top-down control over sensory-driven processes.
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Affiliation(s)
- Jonathan Daume
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Center for Neural Science and Medicine, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
| | - Jan Kamiński
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Center of Excellence for Neural Plasticity and Brain Disorders: BRAINCITY, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland
| | - Andrea G P Schjetnan
- Krembil Research Institute and Division of Neurosurgery, University Health Network (UHN), University of Toronto, Toronto, Ontario, Canada
| | - Yousef Salimpour
- Department of Neurosurgery, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Umais Khan
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Michael Kyzar
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Chrystal M Reed
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - William S Anderson
- Department of Neurosurgery, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Taufik A Valiante
- Krembil Research Institute and Division of Neurosurgery, University Health Network (UHN), University of Toronto, Toronto, Ontario, Canada
| | - Adam N Mamelak
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Ueli Rutishauser
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Center for Neural Science and Medicine, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
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21
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Kay K, Prince JS, Gebhart T, Tuckute G, Zhou J, Naselaris T, Schutt H. Disentangling signal and noise in neural responses through generative modeling. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.22.590510. [PMID: 38712051 PMCID: PMC11071385 DOI: 10.1101/2024.04.22.590510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Measurements of neural responses to identically repeated experimental events often exhibit large amounts of variability. This noise is distinct from signal , operationally defined as the average expected response across repeated trials for each given event. Accurately distinguishing signal from noise is important, as each is a target that is worthy of study (many believe noise reflects important aspects of brain function) and it is important not to confuse one for the other. Here, we introduce a principled modeling approach in which response measurements are explicitly modeled as the sum of samples from multivariate signal and noise distributions. In our proposed method-termed Generative Modeling of Signal and Noise (GSN)-the signal distribution is estimated by subtracting the estimated noise distribution from the estimated data distribution. We validate GSN using ground-truth simulations and demonstrate the application of GSN to empirical fMRI data. In doing so, we illustrate a simple consequence of GSN: by disentangling signal and noise components in neural responses, GSN denoises principal components analysis and improves estimates of dimensionality. We end by discussing other situations that may benefit from GSN's characterization of signal and noise, such as estimation of noise ceilings for computational models of neural activity. A code toolbox for GSN is provided with both MATLAB and Python implementations.
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22
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Koren V, Malerba SB, Schwalger T, Panzeri S. Structure, dynamics, coding and optimal biophysical parameters of efficient excitatory-inhibitory spiking networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.24.590955. [PMID: 38712237 PMCID: PMC11071478 DOI: 10.1101/2024.04.24.590955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
The principle of efficient coding posits that sensory cortical networks are designed to encode maximal sensory information with minimal metabolic cost. Despite the major influence of efficient coding in neuro-science, it has remained unclear whether fundamental empirical properties of neural network activity can be explained solely based on this normative principle. Here, we rigorously derive the structural, coding, biophysical and dynamical properties of excitatory-inhibitory recurrent networks of spiking neurons that emerge directly from imposing that the network minimizes an instantaneous loss function and a time-averaged performance measure enacting efficient coding. The optimal network has biologically-plausible biophysical features, including realistic integrate-and-fire spiking dynamics, spike-triggered adaptation, and a non-stimulus-specific excitatory external input regulating metabolic cost. The efficient network has excitatory-inhibitory recurrent connectivity between neurons with similar stimulus tuning implementing feature-specific competition, similar to that recently found in visual cortex. Networks with unstructured connectivity cannot reach comparable levels of coding efficiency. The optimal biophysical parameters include 4 to 1 ratio of excitatory vs inhibitory neurons and 3 to 1 ratio of mean inhibitory-to-inhibitory vs. excitatory-to-inhibitory connectivity that closely match those of cortical sensory networks. The efficient network has biologically-plausible spiking dynamics, with a tight instantaneous E-I balance that makes them capable to achieve efficient coding of external stimuli varying over multiple time scales. Together, these results explain how efficient coding may be implemented in cortical networks and suggests that key properties of biological neural networks may be accounted for by efficient coding.
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23
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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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24
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Mohammadi M, Carriot J, Mackrous I, Cullen KE, Chacron MJ. Neural populations within macaque early vestibular pathways are adapted to encode natural self-motion. PLoS Biol 2024; 22:e3002623. [PMID: 38687807 PMCID: PMC11086886 DOI: 10.1371/journal.pbio.3002623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 05/10/2024] [Accepted: 04/11/2024] [Indexed: 05/02/2024] Open
Abstract
How the activities of large neural populations are integrated in the brain to ensure accurate perception and behavior remains a central problem in systems neuroscience. Here, we investigated population coding of naturalistic self-motion by neurons within early vestibular pathways in rhesus macaques (Macacca mulatta). While vestibular neurons displayed similar dynamic tuning to self-motion, inspection of their spike trains revealed significant heterogeneity. Further analysis revealed that, during natural but not artificial stimulation, heterogeneity resulted primarily from variability across neurons as opposed to trial-to-trial variability. Interestingly, vestibular neurons displayed different correlation structures during naturalistic and artificial self-motion. Specifically, while correlations due to the stimulus (i.e., signal correlations) did not differ, correlations between the trial-to-trial variabilities of neural responses (i.e., noise correlations) were instead significantly positive during naturalistic but not artificial stimulation. Using computational modeling, we show that positive noise correlations during naturalistic stimulation benefits information transmission by heterogeneous vestibular neural populations. Taken together, our results provide evidence that neurons within early vestibular pathways are adapted to the statistics of natural self-motion stimuli at the population level. We suggest that similar adaptations will be found in other systems and species.
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Affiliation(s)
- Mohammad Mohammadi
- Department of Biological and Biomedical Engineering, McGill University, Montreal, Canada
| | - Jerome Carriot
- Department of Physiology, McGill University, Montreal, Canada
| | | | - Kathleen E. Cullen
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
- Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, Maryland, United States of America
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25
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Luppi AI, Rosas FE, Mediano PAM, Menon DK, Stamatakis EA. Information decomposition and the informational architecture of the brain. Trends Cogn Sci 2024; 28:352-368. [PMID: 38199949 DOI: 10.1016/j.tics.2023.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 11/09/2023] [Accepted: 11/17/2023] [Indexed: 01/12/2024]
Abstract
To explain how the brain orchestrates information-processing for cognition, we must understand information itself. Importantly, information is not a monolithic entity. Information decomposition techniques provide a way to split information into its constituent elements: unique, redundant, and synergistic information. We review how disentangling synergistic and redundant interactions is redefining our understanding of integrative brain function and its neural organisation. To explain how the brain navigates the trade-offs between redundancy and synergy, we review converging evidence integrating the structural, molecular, and functional underpinnings of synergy and redundancy; their roles in cognition and computation; and how they might arise over evolution and development. Overall, disentangling synergistic and redundant information provides a guiding principle for understanding the informational architecture of the brain and cognition.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, University of Cambridge, Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Fernando E Rosas
- Department of Informatics, University of Sussex, Brighton, UK; Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, UK; Centre for Complexity Science, Imperial College London, London, UK; Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK
| | - Pedro A M Mediano
- Department of Computing, Imperial College London, London, UK; Department of Psychology, University of Cambridge, Cambridge, UK
| | - David K Menon
- Department of Medicine, University of Cambridge, Cambridge, UK; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, University of Cambridge, Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
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26
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Noda T, Aschauer DF, Chambers AR, Seiler JPH, Rumpel S. Representational maps in the brain: concepts, approaches, and applications. Front Cell Neurosci 2024; 18:1366200. [PMID: 38584779 PMCID: PMC10995314 DOI: 10.3389/fncel.2024.1366200] [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: 01/05/2024] [Accepted: 03/08/2024] [Indexed: 04/09/2024] Open
Abstract
Neural systems have evolved to process sensory stimuli in a way that allows for efficient and adaptive behavior in a complex environment. Recent technological advances enable us to investigate sensory processing in animal models by simultaneously recording the activity of large populations of neurons with single-cell resolution, yielding high-dimensional datasets. In this review, we discuss concepts and approaches for assessing the population-level representation of sensory stimuli in the form of a representational map. In such a map, not only are the identities of stimuli distinctly represented, but their relational similarity is also mapped onto the space of neuronal activity. We highlight example studies in which the structure of representational maps in the brain are estimated from recordings in humans as well as animals and compare their methodological approaches. Finally, we integrate these aspects and provide an outlook for how the concept of representational maps could be applied to various fields in basic and clinical neuroscience.
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Affiliation(s)
- Takahiro Noda
- Institute of Physiology, Focus Program Translational Neurosciences, University Medical Center, Johannes Gutenberg University-Mainz, Mainz, Germany
| | - Dominik F. Aschauer
- Institute of Physiology, Focus Program Translational Neurosciences, University Medical Center, Johannes Gutenberg University-Mainz, Mainz, Germany
| | - Anna R. Chambers
- Department of Otolaryngology – Head and Neck Surgery, Harvard Medical School, Boston, MA, United States
- Eaton Peabody Laboratories, Massachusetts Eye and Ear Infirmary, Boston, MA, United States
| | - Johannes P.-H. Seiler
- Institute of Physiology, Focus Program Translational Neurosciences, University Medical Center, Johannes Gutenberg University-Mainz, Mainz, Germany
| | - Simon Rumpel
- Institute of Physiology, Focus Program Translational Neurosciences, University Medical Center, Johannes Gutenberg University-Mainz, Mainz, Germany
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27
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Panzeri S, Nili H. The importance of tradeoffs in neural and motor variability. Phys Life Rev 2024; 48:164-166. [PMID: 38237428 DOI: 10.1016/j.plrev.2024.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Accepted: 01/07/2024] [Indexed: 03/05/2024]
Affiliation(s)
- Stefano Panzeri
- Institute for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg- Eppendorf (UKE), Hamburg, Germany.
| | - Hamed Nili
- Institute for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg- Eppendorf (UKE), Hamburg, Germany
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28
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Issa JB, Radvansky BA, Xuan F, Dombeck DA. Lateral entorhinal cortex subpopulations represent experiential epochs surrounding reward. Nat Neurosci 2024; 27:536-546. [PMID: 38272968 PMCID: PMC11097142 DOI: 10.1038/s41593-023-01557-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 12/13/2023] [Indexed: 01/27/2024]
Abstract
During goal-directed navigation, 'what' information, describing the experiences occurring in periods surrounding a reward, can be combined with spatial 'where' information to guide behavior and form episodic memories. This integrative process likely occurs in the hippocampus, which receives spatial information from the medial entorhinal cortex; however, the source of the 'what' information is largely unknown. Here, we show that mouse lateral entorhinal cortex (LEC) represents key experiential epochs during reward-based navigation tasks. We discover separate populations of neurons that signal goal approach and goal departure and a third population signaling reward consumption. When reward location is moved, these populations immediately shift their respective representations of each experiential epoch relative to reward, while optogenetic inhibition of LEC disrupts learning the new reward location. Therefore, the LEC contains a stable code of experiential epochs surrounding and including reward consumption, providing reward-centric information to contextualize the spatial information carried by the medial entorhinal cortex.
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Affiliation(s)
- John B Issa
- Department of Neurobiology, Northwestern University, Evanston, IL, USA
| | - Brad A Radvansky
- Department of Neurobiology, Northwestern University, Evanston, IL, USA
| | - Feng Xuan
- Department of Neurobiology, Northwestern University, Evanston, IL, USA
| | - Daniel A Dombeck
- Department of Neurobiology, Northwestern University, Evanston, IL, USA.
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29
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Barta T, Kostal L. Shared input and recurrency in neural networks for metabolically efficient information transmission. PLoS Comput Biol 2024; 20:e1011896. [PMID: 38394341 PMCID: PMC10917264 DOI: 10.1371/journal.pcbi.1011896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 03/06/2024] [Accepted: 02/07/2024] [Indexed: 02/25/2024] Open
Abstract
Shared input to a population of neurons induces noise correlations, which can decrease the information carried by a population activity. Inhibitory feedback in recurrent neural networks can reduce the noise correlations and thus increase the information carried by the population activity. However, the activity of inhibitory neurons is costly. This inhibitory feedback decreases the gain of the population. Thus, depolarization of its neurons requires stronger excitatory synaptic input, which is associated with higher ATP consumption. Given that the goal of neural populations is to transmit as much information as possible at minimal metabolic costs, it is unclear whether the increased information transmission reliability provided by inhibitory feedback compensates for the additional costs. We analyze this problem in a network of leaky integrate-and-fire neurons receiving correlated input. By maximizing mutual information with metabolic cost constraints, we show that there is an optimal strength of recurrent connections in the network, which maximizes the value of mutual information-per-cost. For higher values of input correlation, the mutual information-per-cost is higher for recurrent networks with inhibitory feedback compared to feedforward networks without any inhibitory neurons. Our results, therefore, show that the optimal synaptic strength of a recurrent network can be inferred from metabolically efficient coding arguments and that decorrelation of the input by inhibitory feedback compensates for the associated increased metabolic costs.
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Affiliation(s)
- Tomas Barta
- Laboratory of Computational Neuroscience, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic
- Neural Coding and Brain Computing Unit, Okinawa Institute of Science and Technology, Onna-son, Okinawa, Japan
| | - Lubomir Kostal
- Laboratory of Computational Neuroscience, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic
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30
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Londei F, Arena G, Ferrucci L, Russo E, Ceccarelli F, Genovesio A. Connecting the dots in the zona incerta: A study of neural assemblies and motifs of inter-area coordination in mice. iScience 2024; 27:108761. [PMID: 38274403 PMCID: PMC10808920 DOI: 10.1016/j.isci.2023.108761] [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: 07/19/2023] [Revised: 10/23/2023] [Accepted: 11/11/2023] [Indexed: 01/27/2024] Open
Abstract
The zona incerta (ZI), a subthalamic area connected to numerous brain regions, has raised clinical interest because its stimulation alleviates the motor symptoms of Parkinson's disease. To explore its coordinative nature, we studied the assembly formation in a dataset of neural recordings in mice and quantified the degree of functional coordination of ZI with other 24 brain areas. We found that the ZI is a highly integrative area. The analysis in terms of "loop-like" motifs, directional assemblies composed of three neurons spanning two areas, has revealed reciprocal functional interactions with reentrant signals that, in most cases, start and end with the activation of ZI units. In support of its proposed integrative role, we found that almost one-third of the ZI's neurons formed assemblies with more than half of the other recorded areas and that loop-like assemblies may stand out as hyper-integrative motifs compared to other types of activation patterns.
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Affiliation(s)
- Fabrizio Londei
- Department of Physiology and Pharmacology, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
- PhD Program in Behavioral Neuroscience, Sapienza University of Rome, Rome, Italy
| | - Giulia Arena
- Department of Physiology and Pharmacology, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
- PhD Program in Behavioral Neuroscience, Sapienza University of Rome, Rome, Italy
| | - Lorenzo Ferrucci
- Department of Physiology and Pharmacology, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
| | - Eleonora Russo
- The BioRobotics Institute, Department of Excellence in Robotics and AI, Scuola Superiore Sant’Anna, 56127 Pisa, Italy
| | - Francesco Ceccarelli
- Department of Physiology and Pharmacology, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
| | - Aldo Genovesio
- Department of Physiology and Pharmacology, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
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31
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Greenidge CD, Scholl B, Yates JL, Pillow JW. Efficient Decoding of Large-Scale Neural Population Responses With Gaussian-Process Multiclass Regression. Neural Comput 2024; 36:175-226. [PMID: 38101329 DOI: 10.1162/neco_a_01630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 08/09/2022] [Indexed: 12/17/2023]
Abstract
Neural decoding methods provide a powerful tool for quantifying the information content of neural population codes and the limits imposed by correlations in neural activity. However, standard decoding methods are prone to overfitting and scale poorly to high-dimensional settings. Here, we introduce a novel decoding method to overcome these limitations. Our approach, the gaussian process multiclass decoder (GPMD), is well suited to decoding a continuous low-dimensional variable from high-dimensional population activity and provides a platform for assessing the importance of correlations in neural population codes. The GPMD is a multinomial logistic regression model with a gaussian process prior over the decoding weights. The prior includes hyperparameters that govern the smoothness of each neuron's decoding weights, allowing automatic pruning of uninformative neurons during inference. We provide a variational inference method for fitting the GPMD to data, which scales to hundreds or thousands of neurons and performs well even in data sets with more neurons than trials. We apply the GPMD to recordings from primary visual cortex in three species: monkey, ferret, and mouse. Our decoder achieves state-of-the-art accuracy on all three data sets and substantially outperforms independent Bayesian decoding, showing that knowledge of the correlation structure is essential for optimal decoding in all three species.
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Affiliation(s)
| | - Benjamin Scholl
- University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, U.S.A.
| | - Jacob L Yates
- University of California, Berkeley, School of Optometry, Berkeley, CA 94720, U.S.A.
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32
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Scagliarini T, Sparacino L, Faes L, Marinazzo D, Stramaglia S. Gradients of O-information highlight synergy and redundancy in physiological applications. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 3:1335808. [PMID: 38264338 PMCID: PMC10803408 DOI: 10.3389/fnetp.2023.1335808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 12/21/2023] [Indexed: 01/25/2024]
Abstract
The study of high order dependencies in complex systems has recently led to the introduction of statistical synergy, a novel quantity corresponding to a form of emergence in which patterns at large scales are not traceable from lower scales. As a consequence, several works in the last years dealt with the synergy and its counterpart, the redundancy. In particular, the O-information is a signed metric that measures the balance between redundant and synergistic statistical dependencies. In spite of its growing use, this metric does not provide insight about the role played by low-order scales in the formation of high order effects. To fill this gap, the framework for the computation of the O-information has been recently expanded introducing the so-called gradients of this metric, which measure the irreducible contribution of a variable (or a group of variables) to the high order informational circuits of a system. Here, we review the theory behind the O-information and its gradients and present the potential of these concepts in the field of network physiology, showing two new applications relevant to brain functional connectivity probed via functional resonance imaging and physiological interactions among the variability of heart rate, arterial pressure, respiration and cerebral blood flow.
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Affiliation(s)
- Tomas Scagliarini
- Dipartimento di Fisica e Astronomia G. Galilei, Università degli Studi di Padova, Padova, Italy
| | - Laura Sparacino
- Dipartimento di Ingegneria, Università di Palermo, Palermo, Italy
| | - Luca Faes
- Dipartimento di Ingegneria, Università di Palermo, Palermo, Italy
| | | | - Sebastiano Stramaglia
- Dipartimento Interateneo di Fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy
- Center of Innovative Technologies for Signal Detection and Processing (TIRES), Università degli Studi di Bari Aldo Moro, Bari, Italy
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33
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Koçillari L, Celotto M, Francis NA, Mukherjee S, Babadi B, Kanold PO, Panzeri S. Behavioural relevance of redundant and synergistic stimulus information between functionally connected neurons in mouse auditory cortex. Brain Inform 2023; 10:34. [PMID: 38052917 PMCID: PMC10697912 DOI: 10.1186/s40708-023-00212-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 11/02/2023] [Indexed: 12/07/2023] Open
Abstract
Measures of functional connectivity have played a central role in advancing our understanding of how information is transmitted and processed within the brain. Traditionally, these studies have focused on identifying redundant functional connectivity, which involves determining when activity is similar across different sites or neurons. However, recent research has highlighted the importance of also identifying synergistic connectivity-that is, connectivity that gives rise to information not contained in either site or neuron alone. Here, we measured redundant and synergistic functional connectivity between neurons in the mouse primary auditory cortex during a sound discrimination task. Specifically, we measured directed functional connectivity between neurons simultaneously recorded with calcium imaging. We used Granger Causality as a functional connectivity measure. We then used Partial Information Decomposition to quantify the amount of redundant and synergistic information about the presented sound that is carried by functionally connected or functionally unconnected pairs of neurons. We found that functionally connected pairs present proportionally more redundant information and proportionally less synergistic information about sound than unconnected pairs, suggesting that their functional connectivity is primarily redundant. Further, synergy and redundancy coexisted both when mice made correct or incorrect perceptual discriminations. However, redundancy was much higher (both in absolute terms and in proportion to the total information available in neuron pairs) in correct behavioural choices compared to incorrect ones, whereas synergy was higher in absolute terms but lower in relative terms in correct than in incorrect behavioural choices. Moreover, the proportion of redundancy reliably predicted perceptual discriminations, with the proportion of synergy adding no extra predictive power. These results suggest a crucial contribution of redundancy to correct perceptual discriminations, possibly due to the advantage it offers for information propagation, and also suggest a role of synergy in enhancing information level during correct discriminations.
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Affiliation(s)
- Loren Koçillari
- 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), Falkenried 94, 20251, Hamburg, Germany.
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf (UKE), 20246, Hamburg, Germany.
| | - Marco Celotto
- 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), Falkenried 94, 20251, Hamburg, Germany
- Department of Pharmacy and Biotechnology, University of Bologna, 40126, Bologna, Italy
| | - Nikolas A Francis
- Department of Biology and Brain and Behavior Institute, University of Maryland, College Park, MD, 20742, USA
| | - Shoutik Mukherjee
- Department of Electrical and Computer Engineering and Institute for Systems Research, University of Maryland, College Park, MD, 20742, USA
| | - Behtash Babadi
- Department of Electrical and Computer Engineering and Institute for Systems Research, University of Maryland, College Park, MD, 20742, USA
| | - Patrick O Kanold
- Department of Biomedical Engineering and Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Stefano Panzeri
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Falkenried 94, 20251, Hamburg, Germany.
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34
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Gusain P, Taketoshi M, Tominaga Y, Tominaga T. Functional Dissection of Ipsilateral and Contralateral Neural Activity Propagation Using Voltage-Sensitive Dye Imaging in Mouse Prefrontal Cortex. eNeuro 2023; 10:ENEURO.0161-23.2023. [PMID: 37977827 DOI: 10.1523/eneuro.0161-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 11/03/2023] [Accepted: 11/10/2023] [Indexed: 11/19/2023] Open
Abstract
Prefrontal cortex (PFC) intrahemispheric activity and the interhemispheric connection have a significant impact on neuropsychiatric disorder pathology. This study aimed to generate a functional map of FC intrahemispheric and interhemispheric connections. Functional dissection of mouse PFCs was performed using the voltage-sensitive dye (VSD) imaging method with high speed (1 ms/frame), high resolution (256 × 256 pixels), and a large field of view (∼10 mm). Acute serial 350 μm slices were prepared from the bregma covering the PFC and numbered 1-5 based on their distance from the bregma (i.e., 1.70, 1.34, 0.98, 0.62, and 0.26 mm) with reference to the Mouse Brain Atlas (Paxinos and Franklin, 2008). The neural response to electrical stimulation was measured at nine sites and then averaged, and a functional map of the propagation patterns was created. Intracortical propagation was observed in slices 3-5, encompassing the anterior cingulate cortex (ACC) and corpus callosum (CC). The activity reached area 33 of the ACC. Direct white matter stimulation activated area 33 in both hemispheres. Similar findings were obtained via DiI staining of the CC. Imaging analysis revealed directional biases in neural signals traveling within the ACC, whereby the signal transmission speed and probability varied based on the signal direction. Specifically, the spread of neural signals from cg2 to cg1 was stronger than that from cingulate cortex area 1(cg1) to cingulate cortex area 2(cg2), which has implications for interhemispheric functional connections. These findings highlight the importance of understanding the PFC functional anatomy in evaluating neuromodulators like serotonin and dopamine, as well as other factors related to neuropsychiatric diseases.
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Affiliation(s)
- Pooja Gusain
- Institute of Neuroscience, Tokushima Bunri University, Sanuki 769-2193, Japan
| | - Makiko Taketoshi
- Institute of Neuroscience, Tokushima Bunri University, Sanuki 769-2193, Japan
| | - Yoko Tominaga
- Institute of Neuroscience, Tokushima Bunri University, Sanuki 769-2193, Japan
| | - Takashi Tominaga
- Institute of Neuroscience, Tokushima Bunri University, Sanuki 769-2193, Japan
- Kagawa School of Pharmaceutical Sciences, Tokushima Bunri University, Sanuki 769-2193, Japan
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35
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Nardin M, Csicsvari J, Tkačik G, Savin C. The Structure of Hippocampal CA1 Interactions Optimizes Spatial Coding across Experience. J Neurosci 2023; 43:8140-8156. [PMID: 37758476 PMCID: PMC10697404 DOI: 10.1523/jneurosci.0194-23.2023] [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: 02/09/2023] [Revised: 09/11/2023] [Accepted: 09/14/2023] [Indexed: 10/03/2023] Open
Abstract
Although much is known about how single neurons in the hippocampus represent an animal's position, how circuit interactions contribute to spatial coding is less well understood. Using a novel statistical estimator and theoretical modeling, both developed in the framework of maximum entropy models, we reveal highly structured CA1 cell-cell interactions in male rats during open field exploration. The statistics of these interactions depend on whether the animal is in a familiar or novel environment. In both conditions the circuit interactions optimize the encoding of spatial information, but for regimes that differ in the informativeness of their spatial inputs. This structure facilitates linear decodability, making the information easy to read out by downstream circuits. Overall, our findings suggest that the efficient coding hypothesis is not only applicable to individual neuron properties in the sensory periphery, but also to neural interactions in the central brain.SIGNIFICANCE STATEMENT Local circuit interactions play a key role in neural computation and are dynamically shaped by experience. However, measuring and assessing their effects during behavior remains a challenge. Here, we combine techniques from statistical physics and machine learning to develop new tools for determining the effects of local network interactions on neural population activity. This approach reveals highly structured local interactions between hippocampal neurons, which make the neural code more precise and easier to read out by downstream circuits, across different levels of experience. More generally, the novel combination of theory and data analysis in the framework of maximum entropy models enables traditional neural coding questions to be asked in naturalistic settings.
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Affiliation(s)
- Michele Nardin
- Institute of Science and Technology Austria, Klosterneuburg AT-3400, Austria
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia 20147
| | - Jozsef Csicsvari
- Institute of Science and Technology Austria, Klosterneuburg AT-3400, Austria
| | - Gašper Tkačik
- Institute of Science and Technology Austria, Klosterneuburg AT-3400, Austria
| | - Cristina Savin
- Center for Neural Science, New York University, New York, New York 10003
- Center for Data Science, New York University, New York, New York 10011
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36
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Weiss O, Bounds HA, Adesnik H, Coen-Cagli R. Modeling the diverse effects of divisive normalization on noise correlations. PLoS Comput Biol 2023; 19:e1011667. [PMID: 38033166 PMCID: PMC10715670 DOI: 10.1371/journal.pcbi.1011667] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 12/12/2023] [Accepted: 11/07/2023] [Indexed: 12/02/2023] Open
Abstract
Divisive normalization, a prominent descriptive model of neural activity, is employed by theories of neural coding across many different brain areas. Yet, the relationship between normalization and the statistics of neural responses beyond single neurons remains largely unexplored. Here we focus on noise correlations, a widely studied pairwise statistic, because its stimulus and state dependence plays a central role in neural coding. Existing models of covariability typically ignore normalization despite empirical evidence suggesting it affects correlation structure in neural populations. We therefore propose a pairwise stochastic divisive normalization model that accounts for the effects of normalization and other factors on covariability. We first show that normalization modulates noise correlations in qualitatively different ways depending on whether normalization is shared between neurons, and we discuss how to infer when normalization signals are shared. We then apply our model to calcium imaging data from mouse primary visual cortex (V1), and find that it accurately fits the data, often outperforming a popular alternative model of correlations. Our analysis indicates that normalization signals are often shared between V1 neurons in this dataset. Our model will enable quantifying the relation between normalization and covariability in a broad range of neural systems, which could provide new constraints on circuit mechanisms of normalization and their role in information transmission and representation.
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Affiliation(s)
- Oren Weiss
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Hayley A. Bounds
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, United States of America
| | - Hillel Adesnik
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, United States of America
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, California, United States of America
| | - Ruben Coen-Cagli
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, United States of America
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York, United States of America
- Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, Bronx, New York, United States of America
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37
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Issa JB, Radvansky BA, Xuan F, Dombeck DA. Lateral entorhinal cortex subpopulations represent experiential epochs surrounding reward. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.09.561557. [PMID: 37873482 PMCID: PMC10592707 DOI: 10.1101/2023.10.09.561557] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
During goal-directed navigation, "what" information, which describes the experiences occurring in periods surrounding a reward, can be combined with spatial "where" information to guide behavior and form episodic memories1,2. This integrative process is thought to occur in the hippocampus3, which receives spatial information from the medial entorhinal cortex (MEC)4; however, the source of the "what" information and how it is represented is largely unknown. Here, by establishing a novel imaging method, we show that the lateral entorhinal cortex (LEC) of mice represents key experiential epochs during a reward-based navigation task. We discover a population of neurons that signals goal approach and a separate population of neurons that signals goal departure. A third population of neurons signals reward consumption. When reward location is moved, these populations immediately shift their respective representations of each experiential epoch relative to reward, while optogenetic inhibition of LEC disrupts learning of the new reward location. Together, these results indicate the LEC provides a stable code of experiential epochs surrounding and including reward consumption, providing reward-centric information to contextualize the spatial information carried by the MEC. Such parallel representations are well-suited for generating episodic memories of rewarding experiences and guiding flexible and efficient goal-directed navigation5-7.
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Affiliation(s)
- John B. Issa
- Department of Neurobiology, Northwestern University, Evanston, IL 60608, USA
| | - Brad A. Radvansky
- Department of Neurobiology, Northwestern University, Evanston, IL 60608, USA
| | - Feng Xuan
- Department of Neurobiology, Northwestern University, Evanston, IL 60608, USA
| | - Daniel A. Dombeck
- Department of Neurobiology, Northwestern University, Evanston, IL 60608, USA
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38
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Ma H, Qi Y, Gong P, Zhang J, Lu WL, Feng J. Self-Organization of Nonlinearly Coupled Neural Fluctuations Into Synergistic Population Codes. Neural Comput 2023; 35:1820-1849. [PMID: 37725705 DOI: 10.1162/neco_a_01612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 06/26/2023] [Indexed: 09/21/2023]
Abstract
Neural activity in the brain exhibits correlated fluctuations that may strongly influence the properties of neural population coding. However, how such correlated neural fluctuations may arise from the intrinsic neural circuit dynamics and subsequently affect the computational properties of neural population activity remains poorly understood. The main difficulty lies in resolving the nonlinear coupling between correlated fluctuations with the overall dynamics of the system. In this study, we investigate the emergence of synergistic neural population codes from the intrinsic dynamics of correlated neural fluctuations in a neural circuit model capturing realistic nonlinear noise coupling of spiking neurons. We show that a rich repertoire of spatial correlation patterns naturally emerges in a bump attractor network and further reveals the dynamical regime under which the interplay between differential and noise correlations leads to synergistic codes. Moreover, we find that negative correlations may induce stable bound states between two bumps, a phenomenon previously unobserved in firing rate models. These noise-induced effects of bump attractors lead to a number of computational advantages including enhanced working memory capacity and efficient spatiotemporal multiplexing and can account for a range of cognitive and behavioral phenomena related to working memory. This study offers a dynamical approach to investigating realistic correlated neural fluctuations and insights to their roles in cortical computations.
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Affiliation(s)
- Hengyuan Ma
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
| | - Yang Qi
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai 200433, China
| | - Pulin Gong
- School of Physics, University of Sydney, Sydney, NSW 2006, Australia
| | - Jie Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai 200433, China
| | - Wen-Lian Lu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai 200433, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai 200433, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai 200433, China
- Department of Computer Science, University of Warwick, Coventry, CV4 7AL, U.K.
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39
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Jerjian SJ, Harsch DR, Fetsch CR. Self-motion perception and sequential decision-making: where are we heading? Philos Trans R Soc Lond B Biol Sci 2023; 378:20220333. [PMID: 37545301 PMCID: PMC10404932 DOI: 10.1098/rstb.2022.0333] [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: 03/27/2023] [Accepted: 06/18/2023] [Indexed: 08/08/2023] Open
Abstract
To navigate and guide adaptive behaviour in a dynamic environment, animals must accurately estimate their own motion relative to the external world. This is a fundamentally multisensory process involving integration of visual, vestibular and kinesthetic inputs. Ideal observer models, paired with careful neurophysiological investigation, helped to reveal how visual and vestibular signals are combined to support perception of linear self-motion direction, or heading. Recent work has extended these findings by emphasizing the dimension of time, both with regard to stimulus dynamics and the trade-off between speed and accuracy. Both time and certainty-i.e. the degree of confidence in a multisensory decision-are essential to the ecological goals of the system: terminating a decision process is necessary for timely action, and predicting one's accuracy is critical for making multiple decisions in a sequence, as in navigation. Here, we summarize a leading model for multisensory decision-making, then show how the model can be extended to study confidence in heading discrimination. Lastly, we preview ongoing efforts to bridge self-motion perception and navigation per se, including closed-loop virtual reality and active self-motion. The design of unconstrained, ethologically inspired tasks, accompanied by large-scale neural recordings, raise promise for a deeper understanding of spatial perception and decision-making in the behaving animal. This article is part of the theme issue 'Decision and control processes in multisensory perception'.
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Affiliation(s)
- Steven J. Jerjian
- Solomon H. Snyder Department of Neuroscience, Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Devin R. Harsch
- Solomon H. Snyder Department of Neuroscience, Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD 21218, USA
- Center for Neuroscience and Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Christopher R. Fetsch
- Solomon H. Snyder Department of Neuroscience, Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD 21218, USA
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40
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Safaai H, Wang AY, Kira S, Malerba SB, Panzeri S, Harvey CD. Specialized structure of neural population codes in parietal cortex outputs. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.24.554635. [PMID: 37662297 PMCID: PMC10473762 DOI: 10.1101/2023.08.24.554635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Do cortical neurons that send axonal projections to the same target area form specialized population codes for transmitting information? We used calcium imaging in mouse posterior parietal cortex (PPC), retrograde labeling, and statistical multivariate models to address this question during a delayed match-to-sample task. We found that PPC broadcasts sensory, choice, and locomotion signals widely, but sensory information is enriched in the output to anterior cingulate cortex. Neurons projecting to the same area have elevated pairwise activity correlations. These correlations are structured as information-limiting and information-enhancing interaction networks that collectively enhance information levels. This network structure is unique to sub-populations projecting to the same target and strikingly absent in surrounding neural populations with unidentified projections. Furthermore, this structure is only present when mice make correct, but not incorrect, behavioral choices. Therefore, cortical neurons comprising an output pathway form uniquely structured population codes that enhance information transmission to guide accurate behavior.
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Affiliation(s)
- Houman Safaai
- Department of Neurobiology, Harvard Medical School, Boston, USA
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Genova, Italy
| | - Alice Y. Wang
- Department of Neurobiology, Harvard Medical School, Boston, USA
| | - Shinichiro Kira
- Department of Neurobiology, Harvard Medical School, Boston, USA
| | - Simone Blanco Malerba
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Stefano Panzeri
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Genova, Italy
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
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41
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Metzen MG, Chacron MJ. Descending pathways increase sensory neural response heterogeneity to facilitate decoding and behavior. iScience 2023; 26:107139. [PMID: 37416462 PMCID: PMC10320509 DOI: 10.1016/j.isci.2023.107139] [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: 03/10/2023] [Revised: 04/25/2023] [Accepted: 06/12/2023] [Indexed: 07/08/2023] Open
Abstract
The functional role of heterogeneous spiking responses of otherwise similarly tuned neurons to stimulation, which has been observed ubiquitously, remains unclear to date. Here, we demonstrate that such response heterogeneity serves a beneficial function that is used by downstream brain areas to generate behavioral responses that follows the detailed timecourse of the stimulus. Multi-unit recordings from sensory pyramidal cells within the electrosensory system of Apteronotus leptorhynchus were performed and revealed highly heterogeneous responses that were similar for all cell types. By comparing the coding properties of a given neural population before and after inactivation of descending pathways, we found that heterogeneities were beneficial as decoding was then more robust to the addition of noise. Taken together, our results not only reveal that descending pathways actively promote response heterogeneity within a given cell type, but also uncover a beneficial function for such heterogeneity that is used by the brain to generate behavior.
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Affiliation(s)
- Michael G. Metzen
- Department of Physiology, McGill University, Montreal, QC H3G 1Y6, Canada
| | - Maurice J. Chacron
- Department of Physiology, McGill University, Montreal, QC H3G 1Y6, Canada
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42
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Wakhloo AJ, Sussman TJ, Chung S. Linear Classification of Neural Manifolds with Correlated Variability. PHYSICAL REVIEW LETTERS 2023; 131:027301. [PMID: 37505944 DOI: 10.1103/physrevlett.131.027301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 03/03/2023] [Accepted: 04/21/2023] [Indexed: 07/30/2023]
Abstract
Understanding how the statistical and geometric properties of neural activity relate to performance is a key problem in theoretical neuroscience and deep learning. Here, we calculate how correlations between object representations affect the capacity, a measure of linear separability. We show that for spherical object manifolds, introducing correlations between centroids effectively pushes the spheres closer together, while introducing correlations between the axes effectively shrinks their radii, revealing a duality between correlations and geometry with respect to the problem of classification. We then apply our results to accurately estimate the capacity of deep network data.
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Affiliation(s)
- Albert J Wakhloo
- Center for Computational Neuroscience, Flatiron Institute, 162 Fifth Avenue, New York, New York 10010, USA
- Department of Child and Adolescent Psychiatry, New York State Psychiatric Institute, 1051 Riverside Drive, New York, New York 10032, USA
| | - Tamara J Sussman
- Department of Child and Adolescent Psychiatry, New York State Psychiatric Institute, 1051 Riverside Drive, New York, New York 10032, USA
- Columbia University Irving Medical College, 630 West 168th Street, New York, New York 10032, USA
| | - SueYeon Chung
- Center for Computational Neuroscience, Flatiron Institute, 162 Fifth Avenue, New York, New York 10010, USA
- Center for Neural Science, New York University, 4 Washington Place, New York, New York 10003, USA
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43
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Mitskopoulos L, Onken A. Discovering Low-Dimensional Descriptions of Multineuronal Dependencies. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1026. [PMID: 37509973 PMCID: PMC10378554 DOI: 10.3390/e25071026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/12/2023] [Accepted: 07/04/2023] [Indexed: 07/30/2023]
Abstract
Coordinated activity in neural populations is crucial for information processing. Shedding light on the multivariate dependencies that shape multineuronal responses is important to understand neural codes. However, existing approaches based on pairwise linear correlations are inadequate at capturing complicated interaction patterns and miss features that shape aspects of the population function. Copula-based approaches address these shortcomings by extracting the dependence structures in the joint probability distribution of population responses. In this study, we aimed to dissect neural dependencies with a C-Vine copula approach coupled with normalizing flows for estimating copula densities. While this approach allows for more flexibility compared to fitting parametric copulas, drawing insights on the significance of these dependencies from large sets of copula densities is challenging. To alleviate this challenge, we used a weighted non-negative matrix factorization procedure to leverage shared latent features in neural population dependencies. We validated the method on simulated data and applied it on copulas we extracted from recordings of neurons in the mouse visual cortex as well as in the macaque motor cortex. Our findings reveal that neural dependencies occupy low-dimensional subspaces, but distinct modules are synergistically combined to give rise to diverse interaction patterns that may serve the population function.
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Affiliation(s)
| | - Arno Onken
- School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK
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44
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Marquez MM, Chacron MJ. Serotonin increases population coding of behaviorally relevant stimuli by enhancing responses of ON but not OFF-type sensory neurons. Heliyon 2023; 9:e18315. [PMID: 37539191 PMCID: PMC10395545 DOI: 10.1016/j.heliyon.2023.e18315] [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: 01/30/2023] [Revised: 07/05/2023] [Accepted: 07/13/2023] [Indexed: 08/05/2023] Open
Abstract
How neural populations encode sensory input to generate behavioral responses remains a central problem in systems neuroscience. Here we investigated how neuromodulation influences population coding of behaviorally relevant stimuli to give rise to behavior in the electrosensory system of the weakly electric fish Apteronotus leptorhynchus. We performed multi-unit recordings from ON and OFF sensory pyramidal cells in response to stimuli whose amplitude (i.e., envelope) varied in time, before and after electrical stimulation of the raphe nuclei. Overall, raphe stimulation increased population coding by ON- but not by OFF-type cells, despite both cell types showing similar sensitivities to the stimulus at the single neuron level. Surprisingly, only changes in population coding by ON-type cells were correlated with changes in behavioral responses. Taken together, our results show that neuromodulation differentially affects ON vs. OFF-type cells in order to enhance perception of behaviorally relevant sensory input.
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45
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Scaliti E, Pullar K, Borghini G, Cavallo A, Panzeri S, Becchio C. Kinematic priming of action predictions. Curr Biol 2023:S0960-9822(23)00687-5. [PMID: 37339628 DOI: 10.1016/j.cub.2023.05.055] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 04/06/2023] [Accepted: 05/24/2023] [Indexed: 06/22/2023]
Abstract
The ability to anticipate what others will do next is crucial for navigating social, interactive environments. Here, we develop an experimental and analytical framework to measure the implicit readout of prospective intention information from movement kinematics. Using a primed action categorization task, we first demonstrate implicit access to intention information by establishing a novel form of priming, which we term kinematic priming: subtle differences in movement kinematics prime action prediction. Next, using data collected from the same participants in a forced-choice intention discrimination task 1 h later, we quantify single-trial intention readout-the amount of intention information read by individual perceivers in individual kinematic primes-and assess whether it can be used to predict the amount of kinematic priming. We demonstrate that the amount of kinematic priming, as indexed by both response times (RTs) and initial fixations to a given probe, is directly proportional to the amount of intention information read by the individual perceiver at the single-trial level. These results demonstrate that human perceivers have rapid, implicit access to intention information encoded in movement kinematics and highlight the potential of our approach to reveal the computations that permit the readout of this information with single-subject, single-trial resolution.
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Affiliation(s)
- Eugenio Scaliti
- Center for Human Technologies, Fondazione Istituto Italiano di Tecnologia, Via Enrico Melen, 83, 16152 Genova, Italy; Department of Neurology, University Medical Center Hamburg-Eppendorf (UKE), Martinistrasse 52, 20246 Hamburg, Germany
| | - Kiri Pullar
- Center for Human Technologies, Fondazione Istituto Italiano di Tecnologia, Via Enrico Melen, 83, 16152 Genova, Italy
| | - Giulia Borghini
- Center for Human Technologies, Fondazione Istituto Italiano di Tecnologia, Via Enrico Melen, 83, 16152 Genova, Italy
| | - Andrea Cavallo
- Center for Human Technologies, Fondazione Istituto Italiano di Tecnologia, Via Enrico Melen, 83, 16152 Genova, Italy; Department of Psychology, Università degli Studi di Torino, Via Giuseppe Verdi, 10, 10124 Torino, Italy
| | - Stefano Panzeri
- Center for Human Technologies, Fondazione Istituto Italiano di Tecnologia, Via Enrico Melen, 83, 16152 Genova, Italy; Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Falkenried 94, 20251 Hamburg, Germany.
| | - Cristina Becchio
- Center for Human Technologies, Fondazione Istituto Italiano di Tecnologia, Via Enrico Melen, 83, 16152 Genova, Italy; Department of Neurology, University Medical Center Hamburg-Eppendorf (UKE), Martinistrasse 52, 20246 Hamburg, Germany.
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46
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Celotto M, Bím J, Tlaie A, De Feo V, Lemke S, Chicharro D, Nili H, Bieler M, Hanganu-Opatz IL, Donner TH, Brovelli A, Panzeri S. An information-theoretic quantification of the content of communication between brain regions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.14.544903. [PMID: 37398375 PMCID: PMC10312682 DOI: 10.1101/2023.06.14.544903] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Quantifying the amount, content and direction of communication between brain regions is key to understanding brain function. Traditional methods to analyze brain activity based on the Wiener-Granger causality principle quantify the overall information propagated by neural activity between simultaneously recorded brain regions, but do not reveal the information flow about specific features of interest (such as sensory stimuli). Here, we develop a new information theoretic measure termed Feature-specific Information Transfer (FIT), quantifying how much information about a specific feature flows between two regions. FIT merges the Wiener-Granger causality principle with information-content specificity. We first derive FIT and prove analytically its key properties. We then illustrate and test them with simulations of neural activity, demonstrating that FIT identifies, within the total information flowing between regions, the information that is transmitted about specific features. We then analyze three neural datasets obtained with different recording methods, magneto- and electro-encephalography, and spiking activity, to demonstrate the ability of FIT to uncover the content and direction of information flow between brain regions beyond what can be discerned with traditional anaytical methods. FIT can improve our understanding of how brain regions communicate by uncovering previously hidden feature-specific information flow.
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Affiliation(s)
- Marco Celotto
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto (TN), Italy
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Jan Bím
- Datamole, s. r. o, Vitezne namesti 577/2 Dejvice, 160 00 Praha 6, The Czech Republic
| | - Alejandro Tlaie
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto (TN), Italy
| | - Vito De Feo
- Artificial Intelligence Team, Future Health Technology, and Brain-Computer Interfaces laboratories, School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK
| | - Stefan Lemke
- Department of Cell Biology and Physiology, University of North Carolina, Chapel Hill, United States
| | - Daniel Chicharro
- Department of Computer Science, City, University of London, London, UK
| | - Hamed Nili
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Malte Bieler
- Mobile Technology Lab, School of Economics, Innovation and Technology, University College Kristiania, Oslo, Norway
| | - Ileana L. Hanganu-Opatz
- Institute of Developmental Neurophysiology, Center for Molecular Neurobiology, University Medical Center, Hamburg-Eppendorf, Hamburg, Germany
| | - Tobias H. Donner
- Section Computational Cognitive Neuroscience, Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Andrea Brovelli
- Institut de Neurosciences de la Timone, UMR 7289, Aix Marseille Université, CNRS, Marseille, France
| | - Stefano Panzeri
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Rovereto (TN), Italy
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47
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Lestang JH, Cai H, Averbeck BB, Cohen YE. Functional network properties of the auditory cortex. Hear Res 2023; 433:108768. [PMID: 37075536 PMCID: PMC10205700 DOI: 10.1016/j.heares.2023.108768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 03/27/2023] [Accepted: 04/11/2023] [Indexed: 04/21/2023]
Abstract
The auditory system transforms auditory stimuli from the external environment into perceptual auditory objects. Recent studies have focused on the contribution of the auditory cortex to this transformation. Other studies have yielded important insights into the contributions of neural activity in the auditory cortex to cognition and decision-making. However, despite this important work, the relationship between auditory-cortex activity and behavior/perception has not been fully elucidated. Two of the more important gaps in our understanding are (1) the specific and differential contributions of different fields of the auditory cortex to auditory perception and behavior and (2) the way networks of auditory neurons impact and facilitate auditory information processing. Here, we focus on recent work from non-human-primate models of hearing and review work related to these gaps and put forth challenges to further our understanding of how single-unit activity and network activity in different cortical fields contribution to behavior and perception.
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Affiliation(s)
- Jean-Hugues Lestang
- Departments of Otorhinolaryngology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Huaizhen Cai
- Departments of Otorhinolaryngology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Bruno B Averbeck
- Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, USA.
| | - Yale E Cohen
- Departments of Otorhinolaryngology, University of Pennsylvania, Philadelphia, PA 19104, USA; Neuroscience, University of Pennsylvania, Philadelphia, PA 19104, USA; Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
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48
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Bonato J, Curreli S, Romanzi S, Panzeri S, Fellin T. ASTRA: a deep learning algorithm for fast semantic segmentation of large-scale astrocytic networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.03.539211. [PMID: 37205519 PMCID: PMC10187152 DOI: 10.1101/2023.05.03.539211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Changes in the intracellular calcium concentration are a fundamental fingerprint of astrocytes, the main type of glial cell. Astrocyte calcium signals can be measured with two-photon microscopy, occur in anatomically restricted subcellular regions, and are coordinated across astrocytic networks. However, current analytical tools to identify the astrocytic subcellular regions where calcium signals occur are time-consuming and extensively rely on user-defined parameters. These limitations limit reproducibility and prevent scalability to large datasets and fields-of-view. Here, we present Astrocytic calcium Spatio-Temporal Rapid Analysis (ASTRA), a novel software combining deep learning with image feature engineering for fast and fully automated semantic segmentation of two-photon calcium imaging recordings of astrocytes. We applied ASTRA to several two-photon microscopy datasets and found that ASTRA performed rapid detection and segmentation of astrocytic cell somata and processes with performance close to that of human experts, outperformed state-of-the-art algorithms for the analysis of astrocytic and neuronal calcium data, and generalized across indicators and acquisition parameters. We also applied ASTRA to the first report of two-photon mesoscopic imaging of hundreds of astrocytes in awake mice, documenting large-scale redundant and synergistic interactions in extended astrocytic networks. ASTRA is a powerful tool enabling closed-loop and large-scale reproducible investigation of astrocytic morphology and function.
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Affiliation(s)
- Jacopo Bonato
- Neural Coding Laboratory, Istituto Italiano di Tecnologia; 16163 Genova, Italy
- Department of Pharmacy and Biotechnology, University of Bologna; 40126 Bologna, Italy
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Falkenried 94, D-20251 Hamburg, Germany
| | - Sebastiano Curreli
- Neural Coding Laboratory, Istituto Italiano di Tecnologia; 16163 Genova, Italy
- Optical Approaches to Brain Function Laboratory, Istituto Italiano di Tecnologia; 16163 Genova, Italy
| | - Sara Romanzi
- Neural Coding Laboratory, Istituto Italiano di Tecnologia; 16163 Genova, Italy
- Optical Approaches to Brain Function Laboratory, Istituto Italiano di Tecnologia; 16163 Genova, Italy
- University of Genova; 16126 Genova, Italy
| | - Stefano Panzeri
- Neural Coding Laboratory, Istituto Italiano di Tecnologia; 16163 Genova, Italy
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Falkenried 94, D-20251 Hamburg, Germany
| | - Tommaso Fellin
- Neural Coding Laboratory, Istituto Italiano di Tecnologia; 16163 Genova, Italy
- Optical Approaches to Brain Function Laboratory, Istituto Italiano di Tecnologia; 16163 Genova, Italy
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49
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Rolls ET. Hippocampal spatial view cells for memory and navigation, and their underlying connectivity in humans. Hippocampus 2023; 33:533-572. [PMID: 36070199 PMCID: PMC10946493 DOI: 10.1002/hipo.23467] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 08/16/2022] [Accepted: 08/16/2022] [Indexed: 01/08/2023]
Abstract
Hippocampal and parahippocampal gyrus spatial view neurons in primates respond to the spatial location being looked at. The representation is allocentric, in that the responses are to locations "out there" in the world, and are relatively invariant with respect to retinal position, eye position, head direction, and the place where the individual is located. The underlying connectivity in humans is from ventromedial visual cortical regions to the parahippocampal scene area, leading to the theory that spatial view cells are formed by combinations of overlapping feature inputs self-organized based on their closeness in space. Thus, although spatial view cells represent "where" for episodic memory and navigation, they are formed by ventral visual stream feature inputs in the parahippocampal gyrus in what is the parahippocampal scene area. A second "where" driver of spatial view cells are parietal inputs, which it is proposed provide the idiothetic update for spatial view cells, used for memory recall and navigation when the spatial view details are obscured. Inferior temporal object "what" inputs and orbitofrontal cortex reward inputs connect to the human hippocampal system, and in macaques can be associated in the hippocampus with spatial view cell "where" representations to implement episodic memory. Hippocampal spatial view cells also provide a basis for navigation to a series of viewed landmarks, with the orbitofrontal cortex reward inputs to the hippocampus providing the goals for navigation, which can then be implemented by hippocampal connectivity in humans to parietal cortex regions involved in visuomotor actions in space. The presence of foveate vision and the highly developed temporal lobe for object and scene processing in primates including humans provide a basis for hippocampal spatial view cells to be key to understanding episodic memory in the primate and human hippocampus, and the roles of this system in primate including human navigation.
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Affiliation(s)
- Edmund T. Rolls
- Oxford Centre for Computational NeuroscienceOxfordUK
- Department of Computer ScienceUniversity of WarwickCoventryUK
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50
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Khoury CF, Fala NG, Runyan CA. Arousal and Locomotion Differently Modulate Activity of Somatostatin Neurons across Cortex. eNeuro 2023; 10:ENEURO.0136-23.2023. [PMID: 37169583 PMCID: PMC10216262 DOI: 10.1523/eneuro.0136-23.2023] [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: 04/26/2023] [Revised: 04/28/2023] [Accepted: 05/03/2023] [Indexed: 05/13/2023] Open
Abstract
Arousal powerfully influences cortical activity, in part by modulating local inhibitory circuits. Somatostatin (SOM)-expressing inhibitory interneurons are particularly well situated to shape local population activity in response to shifts in arousal, yet the relationship between arousal state and SOM activity has not been characterized outside of sensory cortex. To determine whether SOM activity is similarly modulated by behavioral state across different levels of the cortical processing hierarchy, we compared the behavioral modulation of SOM-expressing neurons in auditory cortex (AC), a primary sensory region, and posterior parietal cortex (PPC), an association-level region of cortex, in mice. Behavioral state modulated activity differently in AC and PPC. In PPC, transitions to high arousal were accompanied by large increases in activity across the full PPC neural population, especially in SOM neurons. In AC, arousal transitions led to more subtle changes in overall activity, as individual SOM and Non-SOM neurons could be either positively or negatively modulated during transitions to high arousal states. The coding of sensory information in population activity was enhanced during periods of high arousal in AC, but not in PPC. Our findings suggest unique relationships between activity in local circuits and arousal across cortex, which may be tailored to the roles of specific cortical regions in sensory processing or the control of behavior.
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Affiliation(s)
- Christine F Khoury
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, Pennsylvania 15213
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania 15260
| | - Noelle G Fala
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania 15260
| | - Caroline A Runyan
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, Pennsylvania 15213
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania 15260
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