Deep neural network models of sensory systems: windows onto the role of task constraints

TitleDeep neural network models of sensory systems: windows onto the role of task constraints
Publication TypeJournal Article
Year of Publication2019
AuthorsKell, AJE, McDermott, JH
JournalCurrent Opinion in Neurobiology
Volume55
Pagination121 - 132
Date Published01/2019
ISSN09594388
Abstract

Sensory neuroscience aims to build models that predict neural responses and perceptual behaviors, and that provide insight into the principles that give rise to them. For decades, artificial neural networks trained to perform perceptual tasks have attracted interest as potential models of neural computation. Only recently, however, have such systems begun to perform at human levels on some real-world tasks. The recent engineering successes of deep learning have led to renewed interest in artificial neural networks as models of the brain. Here we review applications of deep learning to sensory neuroscience, discussing potential limitations and future directions. We highlight the potential uses of deep neural networks to reveal how task performance may constrain neural systems and behavior. In particular, we consider how task-optimized networks can generate hypotheses about neural representations and functional organization in ways that are analogous to traditional ideal observer models.

URLhttps://linkinghub.elsevier.com/retrieve/pii/S0959438818302034
DOI10.1016/j.conb.2019.02.003
Short TitleCurrent Opinion in Neurobiology

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