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Emergence of Visual Center-Periphery Spatial Organization in Deep Convolutional Neural Networks

View ORCID ProfileYalda Mohsenzadeh, Caitlin Mullin, Benjamin Lahner, Aude Oliva
doi: https://doi.org/10.1101/2020.02.19.956748
Yalda Mohsenzadeh
1Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
2Department of Computer Science, The University of Western Ontario, London, ON, Canada
3The Brain and Mind Institute, The University of Western Ontario, London, ON, Canada
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  • For correspondence: ymohsenz@uwo.ca
Caitlin Mullin
4Department of Psychology, Center for Vision Research, York University, Toronto, ON, Canada
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Benjamin Lahner
1Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
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Aude Oliva
1Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
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Abstract

Research at the intersection of computer vision and neuroscience has revealed hierarchical correspondence between layers of deep convolutional neural networks (DCNNs) and cascade of regions along human ventral visual cortex. Recently, studies have uncovered emergence of human interpretable concepts within DCNNs layers trained to identify visual objects and scenes. Here, we asked whether an artificial neural network (with convolutional structure) trained for visual categorization would demonstrate spatial correspondences with human brain regions showing central/peripheral biases. Using representational similarity analysis, we compared activations of convolutional layers of a DCNN trained for object and scene categorization with neural representations in human brain visual regions. Results reveal a brain-like topographical organization in the layers of the DCNN, such that activations of layer-units with central-bias were associated with brain regions with foveal tendencies (e.g. fusiform gyrus), and activations of layer-units with selectivity for image backgrounds were associated with cortical regions showing peripheral preference (e.g. parahippocampal cortex). The emergence of a categorical topographical correspondence between DCNNs and brain regions suggests these models are a good approximation of the perceptual representation generated by biological neural networks.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted February 20, 2020.
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Emergence of Visual Center-Periphery Spatial Organization in Deep Convolutional Neural Networks
Yalda Mohsenzadeh, Caitlin Mullin, Benjamin Lahner, Aude Oliva
bioRxiv 2020.02.19.956748; doi: https://doi.org/10.1101/2020.02.19.956748
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Emergence of Visual Center-Periphery Spatial Organization in Deep Convolutional Neural Networks
Yalda Mohsenzadeh, Caitlin Mullin, Benjamin Lahner, Aude Oliva
bioRxiv 2020.02.19.956748; doi: https://doi.org/10.1101/2020.02.19.956748

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