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      Mouse visual cortex contains a region of enhanced spatial resolution

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          Abstract

          The representation of space in mouse visual cortex was thought to be relatively uniform. Here we reveal, using population receptive-field (pRF) mapping techniques, that mouse visual cortex contains a region in which pRFs are considerably smaller. This region, the “focea,” represents a location in space in front of, and slightly above, the mouse. Using two-photon imaging we show that the smaller pRFs are due to lower scatter of receptive-fields at the focea and an over-representation of binocular regions of space. We show that receptive-fields of single-neurons in areas LM and AL are smaller at the focea and that mice have improved visual resolution in this region of space. Furthermore, freely moving mice make compensatory eye-movements to hold this region in front of them. Our results indicate that mice have spatial biases in their visual processing, a finding that has important implications for the use of the mouse model of vision.

          Abstract

          The representation of space in mouse visual cortex was considered to be relatively uniform. The authors show that mice have improved visual resolution in a cortical region representing a location in space directly in front and slightly above them, showing that the representation of space in mouse visual cortex is non-uniform.

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          Most cited references64

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          DeepLabCut: markerless pose estimation of user-defined body parts with deep learning

          Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other animals are often marked with reflective markers to assist with computer-based tracking, but markers are intrusive, and the number and location of the markers must be determined a priori. Here we present an efficient method for markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results with minimal training data. We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. Remarkably, even when only a small number of frames are labeled (~200), the algorithm achieves excellent tracking performance on test frames that is comparable to human accuracy.
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            A mesoscale connectome of the mouse brain.

            Comprehensive knowledge of the brain's wiring diagram is fundamental for understanding how the nervous system processes information at both local and global scales. However, with the singular exception of the C. elegans microscale connectome, there are no complete connectivity data sets in other species. Here we report a brain-wide, cellular-level, mesoscale connectome for the mouse. The Allen Mouse Brain Connectivity Atlas uses enhanced green fluorescent protein (EGFP)-expressing adeno-associated viral vectors to trace axonal projections from defined regions and cell types, and high-throughput serial two-photon tomography to image the EGFP-labelled axons throughout the brain. This systematic and standardized approach allows spatial registration of individual experiments into a common three dimensional (3D) reference space, resulting in a whole-brain connectivity matrix. A computational model yields insights into connectional strength distribution, symmetry and other network properties. Virtual tractography illustrates 3D topography among interconnected regions. Cortico-thalamic pathway analysis demonstrates segregation and integration of parallel pathways. The Allen Mouse Brain Connectivity Atlas is a freely available, foundational resource for structural and functional investigations into the neural circuits that support behavioural and cognitive processes in health and disease.
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              CircStat: AMATLABToolbox for Circular Statistics

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                Author and article information

                Contributors
                p.roelfsema@nin.knaw.nl
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                29 June 2021
                29 June 2021
                2021
                : 12
                : 4029
                Affiliations
                [1 ]GRID grid.419918.c, ISNI 0000 0001 2171 8263, Department of Vision & Cognition, , Netherlands Institute for Neuroscience, ; Amsterdam, The Netherlands
                [2 ]GRID grid.5590.9, ISNI 0000000122931605, Donders Institute for Brain, Cognition and Behaviour, , Radboud University, ; Nijmegen, The Netherlands
                [3 ]GRID grid.83440.3b, ISNI 0000000121901201, Sainsbury Wellcome Centre for Neural Circuits and Behaviour, , University College London, ; London, UK
                [4 ]GRID grid.5335.0, ISNI 0000000121885934, Department of Physiology, Development and Neuroscience, , University of Cambridge, ; Cambridge, UK
                [5 ]GRID grid.5335.0, ISNI 0000000121885934, Department of Psychology, , University of Cambridge, ; Cambridge, UK
                [6 ]GRID grid.12380.38, ISNI 0000 0004 1754 9227, Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, , VU University, ; Amsterdam, The Netherlands
                [7 ]GRID grid.5650.6, ISNI 0000000404654431, Department of Psychiatry, , Academic Medical Center, ; Amsterdam, The Netherlands
                Author information
                http://orcid.org/0000-0002-2454-0445
                http://orcid.org/0000-0001-6837-5210
                http://orcid.org/0000-0002-1625-0034
                http://orcid.org/0000-0001-5731-579X
                Article
                24311
                10.1038/s41467-021-24311-5
                8242089
                34188047
                916cd4ce-8ba8-424d-99c3-78fc4fb9172d
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 2 October 2020
                : 18 May 2021
                Funding
                Funded by: The work was supported by NWO (ALW grant 823-02-010) and the European Union’s Horizon 2020 and FP7 Research and Innovation Program (grant agreement 7202070 ‘Human Brain Project SGA1, SGA2 and SGA3’’, ERC grant agreement 339490 ‘Cortic_al_gorithms’’ and the Erasmus Mundus “NeuroTime” program) and the Stichting Vrienden van het Herseninstituut. A.F.M. was supported by the Radboud Excellence Initiative. J.P. is a Wellcome Trust and Royal Society Sir Henry Dale Fellow (211258/Z/18/Z).
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                perception,neural circuits,sensory processing,extrastriate cortex,striate cortex
                Uncategorized
                perception, neural circuits, sensory processing, extrastriate cortex, striate cortex

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