@article {4825,
	title = {Large-scale benchmarking of deep neural network models in mouse visual cortex reveals patterns similar to those observed in macaque visual cortex},
	year = {2021},
	abstract = {<p>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{\quotesinglbase}{\"A}{\^o}s 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.</p>
},
	author = {Colin Conwell and David Mayo and Michael Buice and Boris Katz and George Alvarez and Andrei Barbu}
}
@article {1800,
	title = {Population Coding, Correlations, and Functional Connectivity in the mouse visual system with the Cortical Activity Map (CAM)},
	year = {2015},
	month = {09/19/2015},
	type = {Poster},
	abstract = {<p>The Cortical Activity Map will provide neural responses from large sets of simultaneously recorded cells to a diverse set of visual stimuli from awake, behaving mice in multiple layers, regions, and cell types. This data set allows for unprecedented access to population responses and provides a unique opportunity to explore the collective characteristics of neural dynamics. The visual stimuli for CAM include gratings, sparse noise, spatio-temporal noise, simple objects, natural images, and natural movies. We demonstrate the power of this data set by exploring the nature of population coding in the visual system. To assess information processing across visual areas we develop decoders. We analyze and compare the performance of these decoders for each stimulus type. In particular, we compare the performance of correlation based decoders and those with functional connectivity to independent decoders. For more complex stimuli, we use these models as the basis for reconstruction of the visual stimulus. This set of analyses demonstrates that the Cortical Activity Map will be a powerful tool for exploring the joint activity of large populations of neurons.</p>
},
	author = {Michael Buice and Saskia de Vries},
	editor = {Amy Bernard and Brandon Rogeres and Casey White and Chinh Dang and Pete Groblewski and Chris Lau and Cliff Slaughterbeck and Colin Farrell and Derric Williams and Jack Waters and Jed Perkins and Kate Roll and Leonard Kuan and Lydia Ng and Marina Garrett and Natalia Orlova and Shawn Olsen and Sissy Cross and Stefan Mihalas and Thomas Keenan and Wayne Wakeman and John Phillips and Christof Koch and Clay Reid}
}
