Incorporating intrinsic suppression in deep neural networks captures dynamics of adaptation in neurophysiology and perception.

Abstract:

:Adaptation is a fundamental property of sensory systems that can change subjective experiences in the context of recent information. Adaptation has been postulated to arise from recurrent circuit mechanisms or as a consequence of neuronally intrinsic suppression. However, it is unclear whether intrinsic suppression by itself can account for effects beyond reduced responses. Here, we test the hypothesis that complex adaptation phenomena can emerge from intrinsic suppression cascading through a feedforward model of visual processing. A deep convolutional neural network with intrinsic suppression captured neural signatures of adaptation including novelty detection, enhancement, and tuning curve shifts, while producing aftereffects consistent with human perception. When adaptation was trained in a task where repeated input affects recognition performance, an intrinsic mechanism generalized better than a recurrent neural network. Our results demonstrate that feedforward propagation of intrinsic suppression changes the functional state of the network, reproducing key neurophysiological and perceptual properties of adaptation.

journal_name

Sci Adv

journal_title

Science advances

authors

Vinken K,Boix X,Kreiman G

doi

10.1126/sciadv.abd4205

subject

Has Abstract

pub_date

2020-10-14 00:00:00

issue

42

issn

2375-2548

pii

6/42/eabd4205

journal_volume

6

pub_type

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