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Dyballa L, Rudzite AM, Hoseini MS, Thapa M, Stryker MP, Field GD, Zucker SW. Population encoding of stimulus features along the visual hierarchy. Proc Natl Acad Sci U S A 2024; 121:e2317773121. [PMID: 38227668 PMCID: PMC10823231 DOI: 10.1073/pnas.2317773121] [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: 10/12/2023] [Accepted: 12/13/2023] [Indexed: 01/18/2024] Open
Abstract
The retina and primary visual cortex (V1) both exhibit diverse neural populations sensitive to diverse visual features. Yet it remains unclear how neural populations in each area partition stimulus space to span these features. One possibility is that neural populations are organized into discrete groups of neurons, with each group signaling a particular constellation of features. Alternatively, neurons could be continuously distributed across feature-encoding space. To distinguish these possibilities, we presented a battery of visual stimuli to the mouse retina and V1 while measuring neural responses with multi-electrode arrays. Using machine learning approaches, we developed a manifold embedding technique that captures how neural populations partition feature space and how visual responses correlate with physiological and anatomical properties of individual neurons. We show that retinal populations discretely encode features, while V1 populations provide a more continuous representation. Applying the same analysis approach to convolutional neural networks that model visual processing, we demonstrate that they partition features much more similarly to the retina, indicating they are more like big retinas than little brains.
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Affiliation(s)
- Luciano Dyballa
- Department of Computer Science, Yale University, New Haven, CT06511
| | | | - Mahmood S. Hoseini
- Department of Physiology, University of California, San Francisco, CA94143
| | - Mishek Thapa
- Department of Neurobiology, Duke University, Durham, NC27708
- Department of Ophthalmology, David Geffen School of Medicine, Stein Eye Institute, University of California, Los Angeles, CA90095
| | - Michael P. Stryker
- Department of Physiology, University of California, San Francisco, CA94143
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, CA94143
| | - Greg D. Field
- Department of Neurobiology, Duke University, Durham, NC27708
- Department of Ophthalmology, David Geffen School of Medicine, Stein Eye Institute, University of California, Los Angeles, CA90095
| | - Steven W. Zucker
- Department of Computer Science, Yale University, New Haven, CT06511
- Department of Biomedical Engineering, Yale University, New Haven, CT06511
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2
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Purandare C, Mehta M. Mega-scale movie-fields in the mouse visuo-hippocampal network. eLife 2023; 12:RP85069. [PMID: 37910428 PMCID: PMC10619982 DOI: 10.7554/elife.85069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2023] Open
Abstract
Natural visual experience involves a continuous series of related images while the subject is immobile. How does the cortico-hippocampal circuit process a visual episode? The hippocampus is crucial for episodic memory, but most rodent single unit studies require spatial exploration or active engagement. Hence, we investigated neural responses to a silent movie (Allen Brain Observatory) in head-fixed mice without any task or locomotion demands, or rewards. Surprisingly, a third (33%, 3379/10263) of hippocampal -dentate gyrus, CA3, CA1 and subiculum- neurons showed movie-selectivity, with elevated firing in specific movie sub-segments, termed movie-fields, similar to the vast majority of thalamo-cortical (LGN, V1, AM-PM) neurons (97%, 6554/6785). Movie-tuning remained intact in immobile or spontaneously running mice. Visual neurons had >5 movie-fields per cell, but only ~2 in hippocampus. The movie-field durations in all brain regions spanned an unprecedented 1000-fold range: from 0.02s to 20s, termed mega-scale coding. Yet, the total duration of all the movie-fields of a cell was comparable across neurons and brain regions. The hippocampal responses thus showed greater continuous-sequence encoding than visual areas, as evidenced by fewer and broader movie-fields than in visual areas. Consistently, repeated presentation of the movie images in a fixed, but scrambled sequence virtually abolished hippocampal but not visual-cortical selectivity. The preference for continuous, compared to scrambled sequence was eight-fold greater in hippocampal than visual areas, further supporting episodic-sequence encoding. Movies could thus provide a unified way to probe neural mechanisms of episodic information processing and memory, even in immobile subjects, across brain regions, and species.
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Affiliation(s)
- Chinmay Purandare
- Department of Bioengineering, University of California, Los AngelesLos AngelesUnited States
- W.M. Keck Center for Neurophysics, Department of Physics and Astronomy, University of California, Los AngelesLos AngelesUnited States
- Department of Neurology, University of California, Los AngelesLos AngelesUnited States
| | - Mayank Mehta
- W.M. Keck Center for Neurophysics, Department of Physics and Astronomy, University of California, Los AngelesLos AngelesUnited States
- Department of Neurology, University of California, Los AngelesLos AngelesUnited States
- Department of Electrical and Computer Engineering, University of California, Los AngelesLos AngelesUnited States
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3
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Dyballa L, Rudzite AM, Hoseini MS, Thapa M, Stryker MP, Field GD, Zucker SW. Population encoding of stimulus features along the visual hierarchy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.27.545450. [PMID: 37425920 PMCID: PMC10327159 DOI: 10.1101/2023.06.27.545450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
The retina and primary visual cortex (V1) both exhibit diverse neural populations sensitive to diverse visual features. Yet it remains unclear how neural populations in each area partition stimulus space to span these features. One possibility is that neural populations are organized into discrete groups of neurons, with each group signaling a particular constellation of features. Alternatively, neurons could be continuously distributed across feature-encoding space. To distinguish these possibilities, we presented a battery of visual stimuli to mouse retina and V1 while measuring neural responses with multi-electrode arrays. Using machine learning approaches, we developed a manifold embedding technique that captures how neural populations partition feature space and how visual responses correlate with physiological and anatomical properties of individual neurons. We show that retinal populations discretely encode features, while V1 populations provide a more continuous representation. Applying the same analysis approach to convolutional neural networks that model visual processing, we demonstrate that they partition features much more similarly to the retina, indicating they are more like big retinas than little brains.
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Affiliation(s)
| | | | | | - Mishek Thapa
- Department of Neurobiology, Duke University
- Stein Eye Institute, Department of Ophthalmology, David Geffen School of Medicine, University of California, Los Angeles
| | - Michael P. Stryker
- Department of Physiology, University of California, San Francisco
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco
| | - Greg D. Field
- Department of Neurobiology, Duke University
- Stein Eye Institute, Department of Ophthalmology, David Geffen School of Medicine, University of California, Los Angeles
| | - Steven W. Zucker
- Department of Computer Science, Yale University
- Department of Biomedical Engineering, Yale University
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4
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Price BH, Gavornik JP. Efficient Temporal Coding in the Early Visual System: Existing Evidence and Future Directions. Front Comput Neurosci 2022; 16:929348. [PMID: 35874317 PMCID: PMC9298461 DOI: 10.3389/fncom.2022.929348] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 06/13/2022] [Indexed: 01/16/2023] Open
Abstract
While it is universally accepted that the brain makes predictions, there is little agreement about how this is accomplished and under which conditions. Accurate prediction requires neural circuits to learn and store spatiotemporal patterns observed in the natural environment, but it is not obvious how such information should be stored, or encoded. Information theory provides a mathematical formalism that can be used to measure the efficiency and utility of different coding schemes for data transfer and storage. This theory shows that codes become efficient when they remove predictable, redundant spatial and temporal information. Efficient coding has been used to understand retinal computations and may also be relevant to understanding more complicated temporal processing in visual cortex. However, the literature on efficient coding in cortex is varied and can be confusing since the same terms are used to mean different things in different experimental and theoretical contexts. In this work, we attempt to provide a clear summary of the theoretical relationship between efficient coding and temporal prediction, and review evidence that efficient coding principles explain computations in the retina. We then apply the same framework to computations occurring in early visuocortical areas, arguing that data from rodents is largely consistent with the predictions of this model. Finally, we review and respond to criticisms of efficient coding and suggest ways that this theory might be used to design future experiments, with particular focus on understanding the extent to which neural circuits make predictions from efficient representations of environmental statistics.
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5
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Existing function in primary visual cortex is not perturbed by new skill acquisition of a non-matched sensory task. Nat Commun 2022; 13:3638. [PMID: 35752622 PMCID: PMC9233699 DOI: 10.1038/s41467-022-31440-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 06/16/2022] [Indexed: 02/07/2023] Open
Abstract
Acquisition of new skills has the potential to disturb existing network function. To directly assess whether previously acquired cortical function is altered during learning, mice were trained in an abstract task in which selected activity patterns were rewarded using an optical brain-computer interface device coupled to primary visual cortex (V1) neurons. Excitatory neurons were longitudinally recorded using 2-photon calcium imaging. Despite significant changes in local neural activity during task performance, tuning properties and stimulus encoding assessed outside of the trained context were not perturbed. Similarly, stimulus tuning was stable in neurons that remained responsive following a different, visual discrimination training task. However, visual discrimination training increased the rate of representational drift. Our results indicate that while some forms of perceptual learning may modify the contribution of individual neurons to stimulus encoding, new skill learning is not inherently disruptive to the quality of stimulus representation in adult V1.
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6
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Hoy J. Mouse vision: La vie en ultraviolet. Curr Biol 2021; 31:R962-R964. [PMID: 34375602 DOI: 10.1016/j.cub.2021.06.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Visual systems evolve to extract salient information from natural scenes. A new study demonstrates that the specialized organization of ultraviolet-sensitive photoreceptors in the mouse retina optimizes the visibility of aerial predators within the upper visual scenes experienced by mice.
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Affiliation(s)
- Jennifer Hoy
- Department of Biology, University of Nevada Reno, Reno, NV 89557, USA.
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7
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Qiu Y, Zhao Z, Klindt D, Kautzky M, Szatko KP, Schaeffel F, Rifai K, Franke K, Busse L, Euler T. Natural environment statistics in the upper and lower visual field are reflected in mouse retinal specializations. Curr Biol 2021; 31:3233-3247.e6. [PMID: 34107304 DOI: 10.1016/j.cub.2021.05.017] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Revised: 04/06/2021] [Accepted: 05/11/2021] [Indexed: 12/29/2022]
Abstract
Pressures for survival make sensory circuits adapted to a species' natural habitat and its behavioral challenges. Thus, to advance our understanding of the visual system, it is essential to consider an animal's specific visual environment by capturing natural scenes, characterizing their statistical regularities, and using them to probe visual computations. Mice, a prominent visual system model, have salient visual specializations, being dichromatic with enhanced sensitivity to green and UV in the dorsal and ventral retina, respectively. However, the characteristics of their visual environment that likely have driven these adaptations are rarely considered. Here, we built a UV-green-sensitive camera to record footage from mouse habitats. This footage is publicly available as a resource for mouse vision research. We found chromatic contrast to greatly diverge in the upper, but not the lower, visual field. Moreover, training a convolutional autoencoder on upper, but not lower, visual field scenes was sufficient for the emergence of color-opponent filters, suggesting that this environmental difference might have driven superior chromatic opponency in the ventral mouse retina, supporting color discrimination in the upper visual field. Furthermore, the upper visual field was biased toward dark UV contrasts, paralleled by more light-offset-sensitive ganglion cells in the ventral retina. Finally, footage recorded at twilight suggests that UV promotes aerial predator detection. Our findings support that natural scene statistics shaped early visual processing in evolution.
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Affiliation(s)
- Yongrong Qiu
- Institute for Ophthalmic Research, University of Tübingen, 72076 Tübingen, Germany; Centre for Integrative Neuroscience (CIN), University of Tübingen, 72076 Tübingen, Germany; Graduate Training Centre of Neuroscience (GTC), International Max Planck Research School, University of Tübingen, 72076 Tübingen, Germany
| | - Zhijian Zhao
- Institute for Ophthalmic Research, University of Tübingen, 72076 Tübingen, Germany; Centre for Integrative Neuroscience (CIN), University of Tübingen, 72076 Tübingen, Germany
| | - David Klindt
- Institute for Ophthalmic Research, University of Tübingen, 72076 Tübingen, Germany; Centre for Integrative Neuroscience (CIN), University of Tübingen, 72076 Tübingen, Germany; Graduate Training Centre of Neuroscience (GTC), International Max Planck Research School, University of Tübingen, 72076 Tübingen, Germany
| | - Magdalena Kautzky
- Division of Neurobiology, Faculty of Biology, LMU Munich, 82152 Planegg-Martinsried, Germany; Graduate School of Systemic Neurosciences (GSN), LMU Munich, 82152 Planegg-Martinsried, Germany
| | - Klaudia P Szatko
- Institute for Ophthalmic Research, University of Tübingen, 72076 Tübingen, Germany; Centre for Integrative Neuroscience (CIN), University of Tübingen, 72076 Tübingen, Germany; Graduate Training Centre of Neuroscience (GTC), International Max Planck Research School, University of Tübingen, 72076 Tübingen, Germany; Bernstein Centre for Computational Neuroscience, 72076 Tübingen, Germany
| | - Frank Schaeffel
- Institute for Ophthalmic Research, University of Tübingen, 72076 Tübingen, Germany
| | - Katharina Rifai
- Institute for Ophthalmic Research, University of Tübingen, 72076 Tübingen, Germany; Carl Zeiss Vision International GmbH, 73430 Aalen, Germany
| | - Katrin Franke
- Institute for Ophthalmic Research, University of Tübingen, 72076 Tübingen, Germany; Centre for Integrative Neuroscience (CIN), University of Tübingen, 72076 Tübingen, Germany; Bernstein Centre for Computational Neuroscience, 72076 Tübingen, Germany
| | - Laura Busse
- Division of Neurobiology, Faculty of Biology, LMU Munich, 82152 Planegg-Martinsried, Germany; Bernstein Centre for Computational Neuroscience, 82152 Planegg-Martinsried, Germany.
| | - Thomas Euler
- Institute for Ophthalmic Research, University of Tübingen, 72076 Tübingen, Germany; Centre for Integrative Neuroscience (CIN), University of Tübingen, 72076 Tübingen, Germany; Bernstein Centre for Computational Neuroscience, 72076 Tübingen, Germany.
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8
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Tohmi M, Tanabe S, Cang J. Motion Streak Neurons in the Mouse Visual Cortex. Cell Rep 2021; 34:108617. [PMID: 33440151 DOI: 10.1016/j.celrep.2020.108617] [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: 08/31/2020] [Revised: 10/22/2020] [Accepted: 12/17/2020] [Indexed: 10/22/2022] Open
Abstract
Motion streaks are smeared representation of fast-moving objects due to temporal integration. Here, we test for motion streak signals in mice with two-photon calcium imaging. For small dots moving at low speeds, neurons in primary visual cortex (V1) encode the component motion, with preferred direction along the axis perpendicular to their preferred orientation. At high speeds, V1 neurons prefer the direction along the axis parallel to their preferred orientation, as expected for encoding motion streaks. Whereas some V1 neurons (∼20%) display a switch of preferred motion axis with increasing speed, others (>40%) respond specifically to high speeds at the parallel axis. Motion streak neurons are also seen in higher visual lateromedial (LM), anterolateral (AL), and rostrolateral (RL) areas, but with higher transition speeds, and many still prefer the perpendicular axis even with fast motion. Our results thus indicate that diverse motion encoding exists in mouse visual cortex, with intriguing differences among visual areas.
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Affiliation(s)
- Manavu Tohmi
- Department of Biology, University of Virginia, Charlottesville, VA 22904, USA.
| | - Seiji Tanabe
- Department of Psychology, University of Virginia, Charlottesville, VA 22904, USA
| | - Jianhua Cang
- Department of Biology, University of Virginia, Charlottesville, VA 22904, USA; Department of Psychology, University of Virginia, Charlottesville, VA 22904, USA
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9
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Kowalewski NN, Kauttonen J, Stan PL, Jeon BB, Fuchs T, Chase SM, Lee TS, Kuhlman SJ. Development of Natural Scene Representation in Primary Visual Cortex Requires Early Postnatal Experience. Curr Biol 2020; 31:369-380.e5. [PMID: 33220181 DOI: 10.1016/j.cub.2020.10.046] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Revised: 09/10/2020] [Accepted: 10/15/2020] [Indexed: 02/07/2023]
Abstract
The development of the visual system is known to be shaped by early-life experience. To identify response properties that contribute to enhanced natural scene representation, we performed calcium imaging of excitatory neurons in the primary visual cortex (V1) of awake mice raised in three different conditions (standard-reared, dark-reared, and delayed-visual experience) and compared neuronal responses to natural scene features in relation to simpler grating stimuli that varied in orientation and spatial frequency. We assessed population selectivity in the V1 by using decoding methods and found that natural scene discriminability increased by 75% between the ages of 4 and 6 weeks. Both natural scene and grating discriminability were higher in standard-reared animals than in those raised in the dark. This increase in discriminability was accompanied by a reduction in the number of neurons that responded to low-spatial-frequency gratings. At the same time, there was an increase in neuronal preference for natural scenes. Light exposure restricted to a 2- to 4-week window during adulthood did not induce improvements in natural scene or in grating stimulus discriminability. Our results demonstrate that experience reduces the number of neurons needed to effectively encode grating stimuli and that early visual experience enhances natural scene discriminability by directly increasing responsiveness to natural scene features.
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Affiliation(s)
- Nina N Kowalewski
- Department of Biological Sciences, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, PA 15213, USA
| | - Janne Kauttonen
- Department of Biological Sciences, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, PA 15213, USA; Center for the Neural Basis of Cognition, 1400 Locust Street, Pittsburgh, PA 15219, USA
| | - Patricia L Stan
- Center for the Neural Basis of Cognition, 1400 Locust Street, Pittsburgh, PA 15219, USA; University of Pittsburgh Center for Neuroscience, 4400 Fifth Avenue, Pittsburgh, PA 15213, USA
| | - Brian B Jeon
- Department of Biomedical Engineering, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, PA 15213, USA; Neuroscience Institute, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, PA 15213, USA
| | - Thomas Fuchs
- Department of Biological Sciences, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, PA 15213, USA; Center for the Neural Basis of Cognition, 1400 Locust Street, Pittsburgh, PA 15219, USA; Neuroscience Institute, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, PA 15213, USA
| | - Steven M Chase
- Center for the Neural Basis of Cognition, 1400 Locust Street, Pittsburgh, PA 15219, USA; Department of Biomedical Engineering, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, PA 15213, USA; Neuroscience Institute, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, PA 15213, USA
| | - Tai Sing Lee
- Center for the Neural Basis of Cognition, 1400 Locust Street, Pittsburgh, PA 15219, USA; Neuroscience Institute, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, PA 15213, USA; Department of Computer Science, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, PA 15213, USA
| | - Sandra J Kuhlman
- Department of Biological Sciences, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, PA 15213, USA; Center for the Neural Basis of Cognition, 1400 Locust Street, Pittsburgh, PA 15219, USA; University of Pittsburgh Center for Neuroscience, 4400 Fifth Avenue, Pittsburgh, PA 15213, USA; Department of Biomedical Engineering, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, PA 15213, USA; Neuroscience Institute, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, PA 15213, USA.
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10
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Gonçalves PJ, Lueckmann JM, Deistler M, Nonnenmacher M, Öcal K, Bassetto G, Chintaluri C, Podlaski WF, Haddad SA, Vogels TP, Greenberg DS, Macke JH. Training deep neural density estimators to identify mechanistic models of neural dynamics. eLife 2020; 9:e56261. [PMID: 32940606 PMCID: PMC7581433 DOI: 10.7554/elife.56261] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Accepted: 09/16/2020] [Indexed: 01/27/2023] Open
Abstract
Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge with a machine learning tool which uses deep neural density estimators-trained using model simulations-to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features. Our method is scalable in parameters and data features and can rapidly analyze new data after initial training. We demonstrate the power and flexibility of our approach on receptive fields, ion channels, and Hodgkin-Huxley models. We also characterize the space of circuit configurations giving rise to rhythmic activity in the crustacean stomatogastric ganglion, and use these results to derive hypotheses for underlying compensation mechanisms. Our approach will help close the gap between data-driven and theory-driven models of neural dynamics.
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Affiliation(s)
- Pedro J Gonçalves
- Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of MunichMunichGermany
- Max Planck Research Group Neural Systems Analysis, Center of Advanced European Studies and Research (caesar)BonnGermany
| | - Jan-Matthis Lueckmann
- Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of MunichMunichGermany
- Max Planck Research Group Neural Systems Analysis, Center of Advanced European Studies and Research (caesar)BonnGermany
| | - Michael Deistler
- Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of MunichMunichGermany
- Machine Learning in Science, Excellence Cluster Machine Learning, Tübingen UniversityTübingenGermany
| | - Marcel Nonnenmacher
- Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of MunichMunichGermany
- Max Planck Research Group Neural Systems Analysis, Center of Advanced European Studies and Research (caesar)BonnGermany
- Model-Driven Machine Learning, Institute of Coastal Research, Helmholtz Centre GeesthachtGeesthachtGermany
| | - Kaan Öcal
- Max Planck Research Group Neural Systems Analysis, Center of Advanced European Studies and Research (caesar)BonnGermany
- Mathematical Institute, University of BonnBonnGermany
| | - Giacomo Bassetto
- Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of MunichMunichGermany
- Max Planck Research Group Neural Systems Analysis, Center of Advanced European Studies and Research (caesar)BonnGermany
| | - Chaitanya Chintaluri
- Centre for Neural Circuits and Behaviour, University of OxfordOxfordUnited Kingdom
- Institute of Science and Technology AustriaKlosterneuburgAustria
| | - William F Podlaski
- Centre for Neural Circuits and Behaviour, University of OxfordOxfordUnited Kingdom
| | - Sara A Haddad
- Max Planck Institute for Brain ResearchFrankfurtGermany
| | - Tim P Vogels
- Centre for Neural Circuits and Behaviour, University of OxfordOxfordUnited Kingdom
- Institute of Science and Technology AustriaKlosterneuburgAustria
| | - David S Greenberg
- Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of MunichMunichGermany
- Model-Driven Machine Learning, Institute of Coastal Research, Helmholtz Centre GeesthachtGeesthachtGermany
| | - Jakob H Macke
- Computational Neuroengineering, Department of Electrical and Computer Engineering, Technical University of MunichMunichGermany
- Max Planck Research Group Neural Systems Analysis, Center of Advanced European Studies and Research (caesar)BonnGermany
- Machine Learning in Science, Excellence Cluster Machine Learning, Tübingen UniversityTübingenGermany
- Max Planck Institute for Intelligent SystemsTübingenGermany
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11
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Distributed and retinotopically asymmetric processing of coherent motion in mouse visual cortex. Nat Commun 2020; 11:3565. [PMID: 32678087 PMCID: PMC7366664 DOI: 10.1038/s41467-020-17283-5] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Accepted: 06/23/2020] [Indexed: 12/13/2022] Open
Abstract
Perception of visual motion is important for a range of ethological behaviors in mammals. In primates, specific visual cortical regions are specialized for processing of coherent visual motion. However, whether mouse visual cortex has a similar organization remains unclear, despite powerful genetic tools available for measuring population neural activity. Here, we use widefield and 2-photon calcium imaging of transgenic mice to measure mesoscale and cellular responses to coherent motion. Imaging of primary visual cortex (V1) and higher visual areas (HVAs) during presentation of natural movies and random dot kinematograms (RDKs) reveals varied responsiveness to coherent motion, with stronger responses in dorsal stream areas compared to ventral stream areas. Moreover, there is considerable anisotropy within visual areas, such that neurons representing the lower visual field are more responsive to coherent motion. These results indicate that processing of visual motion in mouse cortex is distributed heterogeneously both across and within visual areas.
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12
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Yang L, Lee K, Villagracia J, Masmanidis SC. Open source silicon microprobes for high throughput neural recording. J Neural Eng 2020; 17:016036. [PMID: 31731284 DOI: 10.1088/1741-2552/ab581a] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
OBJECTIVE Microfabricated multielectrode arrays are widely used for high throughput recording of extracellular neural activity, which is transforming our understanding of brain function in health and disease. Currently there is a plethora of electrode-based tools being developed at higher education and research institutions. However, taking such tools from the initial research and development phase to widespread adoption by the neuroscience community is often hindered by several obstacles. The objective of this work is to describe the development, application, and open dissemination of silicon microprobes for recording neural activity in vivo. APPROACH We propose an open source dissemination platform as an alternative to commercialization. This framework promotes recording tools that are openly and inexpensively available to the community. The silicon microprobes are designed in house, but the fabrication and assembly processes are carried out by third party companies. This enables mass production, a key requirement for large-scale dissemination. MAIN RESULTS We demonstrate the operation of silicon microprobes containing up to 256 electrodes in conjunction with optical fibers for optogenetic manipulations or fiber photometry. These data provide new insights about the relationship between calcium activity and neural spiking activity. We also describe the current state of dissemination of these tools. A file repository of resources related to designing, using, and sharing these tools is maintained online. SIGNIFICANCE This paper is likely to be a valuable resource for both current and prospective users, as well as developers of silicon microprobes. Based on their extensive usage by a number of labs including ours, these tools present a promising alternative to other types of electrode-based technologies aimed at high throughput recording in head-fixed animals. This work also demonstrates the importance of validating fiber photometry measurements with simultaneous electrophysiological recordings.
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13
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de Vries SEJ, Lecoq JA, Buice MA, Groblewski PA, Ocker GK, Oliver M, Feng D, Cain N, Ledochowitsch P, Millman D, Roll K, Garrett M, Keenan T, Kuan L, Mihalas S, Olsen S, Thompson C, Wakeman W, Waters J, Williams D, Barber C, Berbesque N, Blanchard B, Bowles N, Caldejon SD, Casal L, Cho A, Cross S, Dang C, Dolbeare T, Edwards M, Galbraith J, Gaudreault N, Gilbert TL, Griffin F, Hargrave P, Howard R, Huang L, Jewell S, Keller N, Knoblich U, Larkin JD, Larsen R, Lau C, Lee E, Lee F, Leon A, Li L, Long F, Luviano J, Mace K, Nguyen T, Perkins J, Robertson M, Seid S, Shea-Brown E, Shi J, Sjoquist N, Slaughterbeck C, Sullivan D, Valenza R, White C, Williford A, Witten DM, Zhuang J, Zeng H, Farrell C, Ng L, Bernard A, Phillips JW, Reid RC, Koch C. A large-scale standardized physiological survey reveals functional organization of the mouse visual cortex. Nat Neurosci 2020; 23:138-151. [PMID: 31844315 PMCID: PMC6948932 DOI: 10.1038/s41593-019-0550-9] [Citation(s) in RCA: 141] [Impact Index Per Article: 35.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Accepted: 10/28/2019] [Indexed: 11/16/2022]
Abstract
To understand how the brain processes sensory information to guide behavior, we must know how stimulus representations are transformed throughout the visual cortex. Here we report an open, large-scale physiological survey of activity in the awake mouse visual cortex: the Allen Brain Observatory Visual Coding dataset. This publicly available dataset includes the cortical activity of nearly 60,000 neurons from six visual areas, four layers, and 12 transgenic mouse lines in a total of 243 adult mice, in response to a systematic set of visual stimuli. We classify neurons on the basis of joint reliabilities to multiple stimuli and validate this functional classification with models of visual responses. While most classes are characterized by responses to specific subsets of the stimuli, the largest class is not reliably responsive to any of the stimuli and becomes progressively larger in higher visual areas. These classes reveal a functional organization wherein putative dorsal areas show specialization for visual motion signals.
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Affiliation(s)
| | | | | | | | | | | | - David Feng
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | - Kate Roll
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Tom Keenan
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Leonard Kuan
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Shawn Olsen
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Jack Waters
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Chris Barber
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | - Linzy Casal
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Andrew Cho
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Sissy Cross
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Chinh Dang
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Tim Dolbeare
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | | | | | | | | | - Sean Jewell
- Department of Statistics, University of Washington, Seattle, WA, USA
| | - Nika Keller
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Ulf Knoblich
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Chris Lau
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Eric Lee
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Felix Lee
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Arielle Leon
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Lu Li
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Fuhui Long
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Kyla Mace
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Jed Perkins
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Sam Seid
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Eric Shea-Brown
- Allen Institute for Brain Science, Seattle, WA, USA
- Department of Applied Mathematics, University of Washington, Seattle, WA, USA
| | - Jianghong Shi
- Department of Applied Mathematics, University of Washington, Seattle, WA, USA
| | | | | | | | - Ryan Valenza
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Casey White
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Daniela M Witten
- Department of Statistics, University of Washington, Seattle, WA, USA
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Jun Zhuang
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Amy Bernard
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - R Clay Reid
- Allen Institute for Brain Science, Seattle, WA, USA
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14
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Yunzab M, Cloherty SL, Ibbotson MR. Comparison of contrast-dependent phase sensitivity in primary visual cortex of mouse, cat and macaque. Neuroreport 2019; 30:960-965. [PMID: 31469724 PMCID: PMC6735947 DOI: 10.1097/wnr.0000000000001307] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Accepted: 07/03/2019] [Indexed: 11/26/2022]
Abstract
Neurones in the primary visual cortex (V1) are classified into simple and complex types. Simple cells are phase-sensitive, that is, they modulate their responses according to the position and brightness polarity of edges in their receptive fields. Complex cells are phase invariant, that is, they respond to edges in their receptive fields regardless of location or brightness polarity. Simple and complex cells are quantified by the degree of sensitivity to the spatial phases of drifting sinusoidal gratings. Some V1 complex cells become more phase-sensitive at low contrasts. Here we use a standardized analysis method for data derived from grating stimuli developed for macaques to reanalyse data previously collected from cats, and also collect and analyse the responses of 73 mouse V1 neurons. The analysis provides the first consistent comparative study of contrast-dependent phase sensitivity in V1 of mouse, cat and macaque monkey.
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Affiliation(s)
- Molis Yunzab
- National Vision Research Institute, Australian College of Optometry, Carlton
- Department of Optometry and Vision Sciences, University of Melbourne, Parkville
| | - Shaun L. Cloherty
- Department of Physiology, Monash University, Clayton, VIC, Australia
| | - Michael R. Ibbotson
- National Vision Research Institute, Australian College of Optometry, Carlton
- Department of Optometry and Vision Sciences, University of Melbourne, Parkville
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15
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Katzner S, Born G, Busse L. V1 microcircuits underlying mouse visual behavior. Curr Opin Neurobiol 2019; 58:191-198. [PMID: 31585332 DOI: 10.1016/j.conb.2019.09.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 08/12/2019] [Accepted: 09/06/2019] [Indexed: 11/29/2022]
Abstract
Visual behavior is based on the concerted activity of neurons in visual areas, where sensory signals are integrated with top-down information. In the past decade, the advent of new tools, such as functional imaging of populations of identified single neurons, high-density electrophysiology, virus-assisted circuit mapping, and precisely timed, cell-type specific manipulations, has advanced our understanding of the neuronal microcircuits underlying visual behavior. Studies in head-fixed mice, where such tools can routinely be applied, begin to provide new insights into the neural code of primary visual cortex (V1) underlying visual perception, and the micro-circuits of attention, predictive processing, and learning.
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Affiliation(s)
- Steffen Katzner
- Division of Neurobiology, Department Biology II, LMU Munich, 82151 Munich, Germany
| | - Gregory Born
- Division of Neurobiology, Department Biology II, LMU Munich, 82151 Munich, Germany; Graduate School of Systemic Neuroscience (GSN), LMU Munich, 82151 Munich, Germany
| | - Laura Busse
- Division of Neurobiology, Department Biology II, LMU Munich, 82151 Munich, Germany; Bernstein Center for Computational Neuroscience, 82151 Munich, Germany.
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