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Franke K, Cai C, Ponder K, Fu J, Sokoloski S, Berens P, Tolias AS. Asymmetric distribution of color-opponent response types across mouse visual cortex supports superior color vision in the sky. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.06.01.543054. [PMID: 37333280 PMCID: PMC10274736 DOI: 10.1101/2023.06.01.543054] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Color is an important visual feature that informs behavior, and the retinal basis for color vision has been studied across various vertebrate species. While many studies have investigated how color information is processed in visual brain areas of primate species, we have limited understanding of how it is organized beyond the retina in other species, including most dichromatic mammals. In this study, we systematically characterized how color is represented in the primary visual cortex (V1) of mice. Using large-scale neuronal recordings and a luminance and color noise stimulus, we found that more than a third of neurons in mouse V1 are color-opponent in their receptive field center, while the receptive field surround predominantly captures luminance contrast. Furthermore, we found that color-opponency is especially pronounced in posterior V1 that encodes the sky, matching the statistics of natural scenes experienced by mice. Using unsupervised clustering, we demonstrate that the asymmetry in color representations across cortex can be explained by an uneven distribution of green-On/UV-Off color-opponent response types that are represented in the upper visual field. Finally, a simple model with natural scene-inspired parametric stimuli shows that green-On/UV-Off color-opponent response types may enhance the detection of "predatory"-like dark UV-objects in noisy daylight scenes. The results from this study highlight the relevance of color processing in the mouse visual system and contribute to our understanding of how color information is organized in the visual hierarchy across species.
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Affiliation(s)
- Katrin Franke
- Department of Ophthalmology, Byers Eye Institute, Stanford University School of Medicine, Stanford, CA, US
- Stanford Bio-X, Stanford University, Stanford, CA, US
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, US
- Department of Neuroscience & Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
| | - Chenchen Cai
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
- Graduate Training Center of Neuroscience, International Max Planck Research School, University of Tübingen, Tübingen, Germany
| | - Kayla Ponder
- Department of Neuroscience & Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
| | - Jiakun Fu
- Department of Neuroscience & Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
| | - Sacha Sokoloski
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
- Hertie Institute for AI in Brain Health, University of Tübingen, Tübingen, Germany
| | - Philipp Berens
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
- Hertie Institute for AI in Brain Health, University of Tübingen, Tübingen, Germany
| | - Andreas S Tolias
- Department of Ophthalmology, Byers Eye Institute, Stanford University School of Medicine, Stanford, CA, US
- Stanford Bio-X, Stanford University, Stanford, CA, US
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, US
- Department of Neuroscience & Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, US
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2
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Kristensen SS, Jörntell H. Differential encoding of temporally evolving color patterns across nearby V1 neurons. Front Cell Neurosci 2023; 17:1249522. [PMID: 37920202 PMCID: PMC10618616 DOI: 10.3389/fncel.2023.1249522] [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: 06/28/2023] [Accepted: 10/05/2023] [Indexed: 11/04/2023] Open
Abstract
Whereas studies of the V1 cortex have focused mainly on neural line orientation preference, color inputs are also known to have a strong presence among these neurons. Individual neurons typically respond to multiple colors and nearby neurons have different combinations of preferred color inputs. However, the computations performed by V1 neurons on such color inputs have not been extensively studied. Here we aimed to address this issue by studying how different V1 neurons encode different combinations of inputs composed of four basic colors. We quantified the decoding accuracy of individual neurons from multi-electrode array recordings, comparing multiple individual neurons located within 2 mm along the vertical axis of the V1 cortex of the anesthetized rat. We found essentially all V1 neurons to be good at decoding spatiotemporal patterns of color inputs and they did so by encoding them in different ways. Quantitative analysis showed that even adjacent neurons encoded the specific input patterns differently, suggesting a local cortical circuitry organization which tends to diversify rather than unify the neuronal responses to each given input. Using different pairs of monocolor inputs, we also found that V1 neocortical neurons had a diversified and rich color opponency across the four colors, which was somewhat surprising given the fact that rodent retina express only two different types of opsins. We propose that the processing of color inputs in V1 cortex is extensively composed of multiple independent circuitry components that reflect abstract functionalities resident in the internal cortical processing rather than the raw sensory information per se.
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Affiliation(s)
| | - Henrik Jörntell
- Neural Basis of Sensorimotor Control, Department of Experimental Medical Science, Lund University, Lund, Sweden
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3
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Lin Y, Zhang XJ, Yang J, Li S, Li L, Lv X, Ma J, Shi SH. Developmental neuronal origin regulates neocortical map formation. Cell Rep 2023; 42:112170. [PMID: 36842085 DOI: 10.1016/j.celrep.2023.112170] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 12/14/2022] [Accepted: 02/10/2023] [Indexed: 02/27/2023] Open
Abstract
Sensory neurons in the neocortex exhibit distinct functional selectivity to constitute the neural map. While neocortical map of the visual cortex in higher mammals is clustered, it displays a striking "salt-and-pepper" pattern in rodents. However, little is known about the origin and basis of the interspersed neocortical map. Here we report that the intricate excitatory neuronal kinship-dependent synaptic connectivity influences precise functional map organization in the mouse primary visual cortex. While sister neurons originating from the same neurogenic radial glial progenitors (RGPs) preferentially develop synapses, cousin neurons derived from amplifying RGPs selectively antagonize horizontal synapse formation. Accordantly, cousin neurons in similar layers exhibit clear functional selectivity differences, contributing to a salt-and-pepper architecture. Removal of clustered protocadherins (cPCDHs), the largest subgroup of the diverse cadherin superfamily, eliminates functional selectivity differences between cousin neurons and alters neocortical map organization. These results suggest that developmental neuronal origin regulates neocortical map formation via cPCDHs.
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Affiliation(s)
- Yang Lin
- IDG/McGovern Institute for Brain Research, Tsinghua-Peking Center for Life Sciences, Beijing Frontier Research Center for Biological Structure, Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Xin-Jun Zhang
- Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Jiajun Yang
- IDG/McGovern Institute for Brain Research, Tsinghua-Peking Center for Life Sciences, Beijing Frontier Research Center for Biological Structure, Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Shuo Li
- IDG/McGovern Institute for Brain Research, Tsinghua-Peking Center for Life Sciences, Beijing Frontier Research Center for Biological Structure, Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Laura Li
- IDG/McGovern Institute for Brain Research, Tsinghua-Peking Center for Life Sciences, Beijing Frontier Research Center for Biological Structure, Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Xiaohui Lv
- IDG/McGovern Institute for Brain Research, Tsinghua-Peking Center for Life Sciences, Beijing Frontier Research Center for Biological Structure, Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Jian Ma
- IDG/McGovern Institute for Brain Research, Tsinghua-Peking Center for Life Sciences, Beijing Frontier Research Center for Biological Structure, Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Song-Hai Shi
- IDG/McGovern Institute for Brain Research, Tsinghua-Peking Center for Life Sciences, Beijing Frontier Research Center for Biological Structure, Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China; Chinese Institute for Brain Research, Beijing, China.
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4
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Zhou W, Ke S, Li W, Yuan J, Li X, Jin R, Jia X, Jiang T, Dai Z, He G, Fang Z, Shi L, Zhang Q, Gong H, Luo Q, Sun W, Li A, Li P. Mapping the Function of Whole-Brain Projection at the Single Neuron Level. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2202553. [PMID: 36228099 PMCID: PMC9685445 DOI: 10.1002/advs.202202553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 09/18/2022] [Indexed: 06/16/2023]
Abstract
Axonal projection conveys neural information. The divergent and diverse projections of individual neurons imply the complexity of information flow. It is necessary to investigate the relationship between the projection and functional information at the single neuron level for understanding the rules of neural circuit assembly, but a gap remains due to a lack of methods to map the function to whole-brain projection. Here an approach is developed to bridge two-photon calcium imaging in vivo with high-resolution whole-brain imaging based on sparse labeling with the genetically encoded calcium indicator GCaMP6. Reliable whole-brain projections are captured by the high-definition fluorescent micro-optical sectioning tomography (HD-fMOST). A cross-modality cell matching is performed and the functional annotation of whole-brain projection at the single-neuron level (FAWPS) is obtained. Applying it to the layer 2/3 (L2/3) neurons in mouse visual cortex, the relationship is investigated between functional preferences and axonal projection features. The functional preference of projection motifs and the correlation between axonal length in MOs and neuronal orientation selectivity, suggest that projection motif-defined neurons form a functionally specific information flow, and the projection strength in specific targets relates to the information clarity. This pipeline provides a new way to understand the principle of neuronal information transmission.
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Affiliation(s)
- Wei Zhou
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Shanshan Ke
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Wenwei Li
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Jing Yuan
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Xiangning Li
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Rui Jin
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Xueyan Jia
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Tao Jiang
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Zimin Dai
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Guannan He
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Zhiwei Fang
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Liang Shi
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Qi Zhang
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
| | - Hui Gong
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Qingming Luo
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical EngineeringHainan UniversityHaikou570228China
| | - Wenzhi Sun
- Chinese Institute for Brain ResearchBeijing102206China
- School of Basic Medical SciencesCapital Medical UniversityBeijing100069China
| | - Anan Li
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Pengcheng Li
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
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5
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Rhim I, Coello-Reyes G, Nauhaus I. Variations in photoreceptor throughput to mouse visual cortex and the unique effects on tuning. Sci Rep 2021; 11:11937. [PMID: 34099749 PMCID: PMC8184960 DOI: 10.1038/s41598-021-90650-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 05/12/2021] [Indexed: 11/24/2022] Open
Abstract
Visual input to primary visual cortex (V1) depends on highly adaptive filtering in the retina. In turn, isolation of V1 computations requires experimental control of retinal adaptation to infer its spatio-temporal-chromatic output. Here, we measure the balance of input to mouse V1, in the anesthetized setup, from the three main photoreceptor opsins-M-opsin, S-opsin, and rhodopsin-as a function of two stimulus dimensions. The first dimension is the level of light adaptation within the mesopic range, which governs the balance of rod and cone inputs to cortex. The second stimulus dimension is retinotopic position, which governs the balance of S- and M-cone opsin input due to the opsin expression gradient in the retina. The fitted model predicts opsin input under arbitrary lighting environments, which provides a much-needed handle on in-vivo studies of the mouse visual system. We use it here to reveal that V1 is rod-mediated in common laboratory settings yet cone-mediated in natural daylight. Next, we compare functional properties of V1 under rod and cone-mediated inputs. The results show that cone-mediated V1 responds to 2.5-fold higher temporal frequencies than rod-mediated V1. Furthermore, cone-mediated V1 has smaller receptive fields, yet similar spatial frequency tuning. V1 responses in rod-deficient (Gnat1-/-) mice confirm that the effects are due to differences in photoreceptor opsin contribution.
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Affiliation(s)
- I Rhim
- Department of Psychology, University of Texas At Austin, 108 E. Dean Keeton, Austin, TX, 78712, USA
- Center for Perceptual Systems, University of Texas At Austin, 108 E. Dean Keeton, Austin, TX, 78712, USA
| | - G Coello-Reyes
- Department of Psychology, University of Texas At Austin, 108 E. Dean Keeton, Austin, TX, 78712, USA
- Center for Perceptual Systems, University of Texas At Austin, 108 E. Dean Keeton, Austin, TX, 78712, USA
| | - I Nauhaus
- Department of Psychology, University of Texas At Austin, 108 E. Dean Keeton, Austin, TX, 78712, USA.
- Department of Neuroscience, University of Texas At Austin, 1 University Station, Stop C7000, Austin, TX, 78712, USA.
- Center for Perceptual Systems, University of Texas At Austin, 108 E. Dean Keeton, Austin, TX, 78712, USA.
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6
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Morimoto MM, Uchishiba E, Saleem AB. Organization of feedback projections to mouse primary visual cortex. iScience 2021; 24:102450. [PMID: 34113813 PMCID: PMC8169797 DOI: 10.1016/j.isci.2021.102450] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 02/01/2021] [Accepted: 04/14/2021] [Indexed: 11/17/2022] Open
Abstract
Top-down, context-dependent modulation of visual processing has been a topic of wide interest, including in mouse primary visual cortex (V1). However, the organization of feedback projections to V1 is relatively unknown. Here, we investigated inputs to mouse V1 by injecting retrograde tracers. We developed a software pipeline that maps labeled cell bodies to corresponding brain areas in the Allen Reference Atlas. We identified more than 24 brain areas that provide inputs to V1 and quantified the relative strength of their projections. We also assessed the organization of the projections, based on either the organization of cell bodies in the source area (topography) or the distribution of projections across V1 (bias). Projections from most higher visual and some nonvisual areas to V1 showed both topography and bias. Such organization of feedback projections to V1 suggests that parts of the visual field are differentially modulated by context, which can be ethologically relevant for a navigating animal.
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Affiliation(s)
- Mai M. Morimoto
- UCL Institute of Behavioural Neuroscience, Department of Experimental Psychology, University College London, London, WC1H 0AP, UK
| | - Emi Uchishiba
- UCL Institute of Behavioural Neuroscience, Department of Experimental Psychology, University College London, London, WC1H 0AP, UK
| | - Aman B. Saleem
- UCL Institute of Behavioural Neuroscience, Department of Experimental Psychology, University College London, London, WC1H 0AP, UK
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7
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Disparity Sensitivity and Binocular Integration in Mouse Visual Cortex Areas. J Neurosci 2020; 40:8883-8899. [PMID: 33051348 DOI: 10.1523/jneurosci.1060-20.2020] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 09/18/2020] [Accepted: 09/22/2020] [Indexed: 01/02/2023] Open
Abstract
Binocular disparity, the difference between the two eyes' images, is a powerful cue to generate the 3D depth percept known as stereopsis. In primates, binocular disparity is processed in multiple areas of the visual cortex, with distinct contributions of higher areas to specific aspects of depth perception. Mice, too, can perceive stereoscopic depth, and neurons in primary visual cortex (V1) and higher-order, lateromedial (LM) and rostrolateral (RL) areas were found to be sensitive to binocular disparity. A detailed characterization of disparity tuning across mouse visual areas is lacking, however, and acquiring such data might help clarifying the role of higher areas for disparity processing and establishing putative functional correspondences to primate areas. We used two-photon calcium imaging in female mice to characterize the disparity tuning properties of neurons in visual areas V1, LM, and RL in response to dichoptically presented binocular gratings, as well as random dot correlograms (RDC). In all three areas, many neurons were tuned to disparity, showing strong response facilitation or suppression at optimal or null disparity, respectively, even in neurons classified as monocular by conventional ocular dominance (OD) measurements. Neurons in higher areas exhibited broader and more asymmetric disparity tuning curves compared with V1, as observed in primate visual cortex. Finally, we probed neurons' sensitivity to true stereo correspondence by comparing responses to correlated RDC (cRDC) and anticorrelated RDC (aRDC). Area LM, akin to primate ventral visual stream areas, showed higher selectivity for correlated stimuli and reduced anticorrelated responses, indicating higher-level disparity processing in LM compared with V1 and RL.SIGNIFICANCE STATEMENT A major cue for inferring 3D depth is disparity between the two eyes' images. Investigating how binocular disparity is processed in the mouse visual system will not only help delineating the role of mouse higher areas for visual processing, but also shed light on how the mammalian brain computes stereopsis. We found that binocular integration is a prominent feature of mouse visual cortex, as many neurons are selectively and strongly modulated by binocular disparity. Comparison of responses to correlated and anticorrelated random dot correlograms (RDC) revealed that lateromedial area (LM) is more selective to correlated stimuli, while less sensitive to anticorrelated stimuli compared with primary visual cortex (V1) and rostrolateral area (RL), suggesting higher-level disparity processing in LM, resembling primate ventral visual stream areas.
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8
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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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9
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Denman DJ, Luviano JA, Ollerenshaw DR, Cross S, Williams D, Buice MA, Olsen SR, Reid RC. Mouse color and wavelength-specific luminance contrast sensitivity are non-uniform across visual space. eLife 2018; 7:e31209. [PMID: 29319502 PMCID: PMC5762155 DOI: 10.7554/elife.31209] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2017] [Accepted: 12/13/2017] [Indexed: 01/10/2023] Open
Abstract
Mammalian visual behaviors, as well as responses in the neural systems underlying these behaviors, are driven by luminance and color contrast. With constantly improving tools for measuring activity in cell-type-specific populations in the mouse during visual behavior, it is important to define the extent of luminance and color information that is behaviorally accessible to the mouse. A non-uniform distribution of cone opsins in the mouse retina potentially complicates both luminance and color sensitivity; opposing gradients of short (UV-shifted) and middle (blue/green) cone opsins suggest that color discrimination and wavelength-specific luminance contrast sensitivity may differ with retinotopic location. Here we ask how well mice can discriminate color and wavelength-specific luminance changes across visuotopic space. We found that mice were able to discriminate color and were able to do so more broadly across visuotopic space than expected from the cone-opsin distribution. We also found wavelength-band-specific differences in luminance sensitivity.
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Affiliation(s)
| | | | | | - Sissy Cross
- Allen Institute for Brain ScienceSeattleUnited States
| | | | | | - Shawn R Olsen
- Allen Institute for Brain ScienceSeattleUnited States
| | - R Clay Reid
- Allen Institute for Brain ScienceSeattleUnited States
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10
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Stabio ME, Sabbah S, Quattrochi LE, Ilardi MC, Fogerson PM, Leyrer ML, Kim MT, Kim I, Schiel M, Renna JM, Briggman KL, Berson DM. The M5 Cell: A Color-Opponent Intrinsically Photosensitive Retinal Ganglion Cell. Neuron 2018; 97:150-163.e4. [PMID: 29249284 PMCID: PMC5757626 DOI: 10.1016/j.neuron.2017.11.030] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2017] [Revised: 10/09/2017] [Accepted: 11/17/2017] [Indexed: 12/19/2022]
Abstract
Intrinsically photosensitive retinal ganglion cells (ipRGCs) combine direct photosensitivity through melanopsin with synaptically mediated drive from classical photoreceptors through bipolar-cell input. Here, we sought to provide a fuller description of the least understood ipRGC type, the M5 cell, and discovered a distinctive functional characteristic-chromatic opponency (ultraviolet excitatory, green inhibitory). Serial electron microscopic reconstructions revealed that M5 cells receive selective UV-opsin drive from Type 9 cone bipolar cells but also mixed cone signals from bipolar Types 6, 7, and 8. Recordings suggest that both excitation and inhibition are driven by the ON channel and that chromatic opponency results from M-cone-driven surround inhibition mediated by wide-field spiking GABAergic amacrine cells. We show that M5 cells send axons to the dLGN and are thus positioned to provide chromatic signals to visual cortex. These findings underscore that melanopsin's influence extends beyond unconscious reflex functions to encompass cortical vision, perhaps including the perception of color.
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Affiliation(s)
- Maureen E Stabio
- Department of Cell & Developmental Biology, University of Colorado School of Medicine, Aurora, CO 80045, USA.
| | - Shai Sabbah
- Department of Neuroscience, Brown University, Providence, RI 02912, USA
| | | | - Marissa C Ilardi
- Department of Neuroscience, Brown University, Providence, RI 02912, USA
| | | | - Megan L Leyrer
- Department of Neuroscience, Brown University, Providence, RI 02912, USA
| | - Min Tae Kim
- Department of Neuroscience, Brown University, Providence, RI 02912, USA
| | - Inkyu Kim
- Department of Neuroscience, Brown University, Providence, RI 02912, USA
| | - Matthew Schiel
- Circuit Dynamics and Connectivity Unit, National Institute of Neurological Disorders and Stroke, Bethesda, MD 20892, USA
| | - Jordan M Renna
- Department of Biology, University of Akron, Akron, OH 44325, USA
| | - Kevin L Briggman
- Circuit Dynamics and Connectivity Unit, National Institute of Neurological Disorders and Stroke, Bethesda, MD 20892, USA
| | - David M Berson
- Department of Neuroscience, Brown University, Providence, RI 02912, USA
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