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Individual differences among deep neural network models

View ORCID ProfileJohannes Mehrer, Courtney J. Spoerer, View ORCID ProfileNikolaus Kriegeskorte, View ORCID ProfileTim C. Kietzmann
doi: https://doi.org/10.1101/2020.01.08.898288
Johannes Mehrer
MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, Cambridgeshire, UK
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  • For correspondence: johannes.mehrer@mrc-cbu.cam.ac.uk t.kietzmann@donders.ru.nl
Courtney J. Spoerer
MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, Cambridgeshire, UK
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Nikolaus Kriegeskorte
Zuckerman Institute, Columbia University, New York, New York, US
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Tim C. Kietzmann
MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, Cambridgeshire, UKDonders Institute for Brain, Cognition and Behaviour, Radbound University, Nijmegen, the Netherlands
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  • For correspondence: johannes.mehrer@mrc-cbu.cam.ac.uk t.kietzmann@donders.ru.nl
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Abstract

Deep neural networks (DNNs) excel at visual recognition tasks and are increasingly used as a modelling framework for neural computations in the primate brain. However, each DNN instance, just like each individual brain, has a unique connectivity and representational profile. Here, we investigate individual differences among DNN instances that arise from varying only the random initialization of the network weights. Using representational similarity analysis, we demonstrate that this minimal change in initial conditions prior to training leads to substantial differences in intermediate and higher-level network representations, despite achieving indistinguishable network-level classification performance. We locate the origins of the effects in an under-constrained alignment of category exemplars, rather than a misalignment of category centroids. Furthermore, while network regularization can increase the consistency of learned representations, considerable differences remain. These results suggest that computational neuroscientists working with DNNs should base their inferences on multiple networks instances instead of single off-the-shelf 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-NC 4.0 International license.
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Posted January 09, 2020.
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Individual differences among deep neural network models
Johannes Mehrer, Courtney J. Spoerer, Nikolaus Kriegeskorte, Tim C. Kietzmann
bioRxiv 2020.01.08.898288; doi: https://doi.org/10.1101/2020.01.08.898288
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Individual differences among deep neural network models
Johannes Mehrer, Courtney J. Spoerer, Nikolaus Kriegeskorte, Tim C. Kietzmann
bioRxiv 2020.01.08.898288; doi: https://doi.org/10.1101/2020.01.08.898288

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