|Title||Large-scale benchmarking of deep neural network models in mouse visual cortex reveals patterns similar to those observed in macaque visual cortex|
|Publication Type||Conference Poster|
|Year of Publication||2021|
|Authors||Conwell, C, Mayo, D, Buice, M, Katz, B, Alvarez, G, Barbu, A|
What is the representational structure of mouse visual cortex and how is it shaped? Mice obviouslyinteract with the world and recognize objects but unlike in primates, a majority of research to date suggests theactivity of their visual cortex may not be so well described by deep neural networks trained for object recognition.Using the Allen Brain Observatory‚Äôs 2-photon calcium-imaging dataset of activity in over 30,000 rodent visualcortical neurons recorded in response to natural scenes, we work to resolve this discrepancy and demonstrate thatmodern neural networks can indeed be used to explain activity in the mouse visual cortex to a more reasonabledegree than previously suggested. In so doing, we elucidate at large scale the properties of networks whichbest match the biological visual system, with both representational similarity analysis and encoding modelscoming to mostly the same conclusions. Our analysis of 30 object recognition architectures (both pretrainedand randomly initialized) from the PyTorch model zoo demonstrates that deeper, thinner residual networks withbypass connections, fewer parameters shared across many convolutions, and higher scores on the ImageNetimage-recognition challenge tend to be more predictive of the neural activations in our sample. Additionally, wefind a significant degree of overlap between the models that best predict macaque visual cortex (as cataloguedby brain-score.org) and those that best predict mouse visual cortex. In concert, these findings help to bolster themouse brain as a viable source of data for the methods that have been successful thus far in the study of monkeybrains, and provide a preliminary set of design targets for building models that can better take advantage of theunparalleled scale, quality, and resolution of data afforded by calcium-imaging in the mouse brain.
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