A large-scale, standardized physiological survey reveals higher order coding throughout the mouse visual cortex

Author:

de Vries Saskia E. J.,Lecoq Jerome,Buice Michael A.,Groblewski Peter A.,Ocker Gabriel K.,Oliver Michael,Feng David,Cain Nicholas,Ledochowitsch Peter,Millman Daniel,Roll Kate,Garrett Marina,Keenan Tom,Kuan Leonard,Mihalas Stefan,Olsen Shawn,Thompson Carol,Wakeman Wayne,Waters Jack,Williams Derric,Barber Chris,Berbesque Nathan,Blanchard Brandon,Bowles Nicholas,Caldejon Shiella,Casal Linzy,Cho Andrew,Cross Sissy,Dang Chinh,Dolbeare Tim,Edwards Melise,Galbraith John,Gaudreault Nathalie,Griffin Fiona,Hargrave Perry,Howard Robert,Huang Lawrence,Jewell Sean,Keller Nika,Knoblich Ulf,Larkin Josh,Larsen Rachael,Lau Chris,Lee Eric,Lee Felix,Leon Arielle,Li Lu,Long Fuhui,Luviano Jennifer,Mace Kyla,Nguyen Thuyanh,Perkins Jed,Robertson Miranda,Seid Sam,Shea-Brown Eric,Shi Jianghong,Sjoquist Nathan,Slaughterbeck Cliff,Sullivan David,Valenza Ryan,White Casey,Williford Ali,Witten Daniela,Zhuang Jun,Zeng Hongkui,Farrell Colin,Ng Lydia,Bernard Amy,Phillips John W.,Reid R. Clay,Koch Christof

Abstract

SummaryTo 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 neural activity in the awake mouse visual cortex: the Allen Brain Observatory Visual Coding dataset. This publicly available dataset includes cortical activity from nearly 60,000 neurons collected from 6 visual areas, 4 layers, and 12 transgenic mouse lines from 221 adult mice, in response to a systematic set of visual stimuli. Using this dataset, we reveal functional differences across these dimensions and show that visual cortical responses are sparse but correlated. Surprisingly, responses to different stimuli are largely independent, e.g. whether a neuron responds to natural scenes provides no information about whether it responds to natural movies or to gratings. We show that these phenomena cannot be explained by standard local filter-based models, but are consistent with multi-layer hierarchical computation, as found in deeper layers of standard convolutional neural networks.

Publisher

Cold Spring Harbor Laboratory

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