Individual differences among deep neural network models

doi:10.1038/s41467-020-19632-w

Paper

Individual differences among deep neural network models

Published Jan 9, 2020 · J. Mehrer, Courtney J. Spoerer, N. Kriegeskorte

Nature Communications

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124

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8

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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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Study Snapshot

Key takeawayIndividual differences in deep neural network models lead to substantial differences in intermediate and higher-level representations, suggesting that computational neuroscientists should use multiple instances for inference rather than a single off-the-shelf network.

PopulationOlder adults (50-71 years)

Sample size24

MethodsObservational

OutcomesBody Mass Index projections

ResultsSocial networks mitigate obesity in older groups.

Individual differences among deep neural network models

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