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Neuroscience theory posits that the brain's visual system coarsely identifiesbroad object categories via neural activation patterns, with similar objectsproducing similar neural responses. Artificial neural networks also haveinternal activation behavior in response to stimuli. We hypothesize thatnetworks exhibiting brain-like activation behavior will demonstrate brain-likecharacteristics, e.g., stronger generalization capabilities. In this paper weintroduce a human-model similarity (HMS) metric, which quantifies thesimilarity of human fMRI and network activation behavior. To calculate HMS,representational dissimilarity matrices (RDMs) are created as abstractions ofactivation behavior, measured by the correlations of activations to stimuluspairs. HMS is then the correlation between the fMRI RDM and the neural networkRDM across all stimulus pairs. We test the metric on unsupervised predictivecoding networks, which specifically model visual perception, and assess themetric for statistical significance over a large range of hyperparameters. Ourexperiments show that networks with increased human-model similarity arecorrelated with better performance on two computer vision tasks: next frameprediction and object matching accuracy. Further, HMS identifies networks withhigh performance on both tasks. An unexpected secondary finding is that themetric can be employed during training as an early-stopping mechanism.

A Neurobiological Evaluation Metric for Neural Network Model Search