Normalization and pooling in hierarchical models of natural images

Luis G. Sanchez-Giraldo, Md Nasir Uddin Laskar, Odelia Schwartz

Research output: Contribution to journalReview articlepeer-review

11 Scopus citations


Divisive normalization and subunit pooling are two canonical classes of computation that have become widely used in descriptive (what) models of visual cortical processing. Normative (why) models from natural image statistics can help constrain the form and parameters of such classes of models. We focus on recent advances in two particular directions, namely deriving richer forms of divisive normalization, and advances in learning pooling from image statistics. We discuss the incorporation of such components into hierarchical models. We consider both hierarchical unsupervised learning from image statistics, and discriminative supervised learning in deep convolutional neural networks (CNNs). We further discuss studies on the utility and extensions of the convolutional architecture, which has also been adopted by recent descriptive models. We review the recent literature and discuss the current promises and gaps of using such approaches to gain a better understanding of how cortical neurons represent and process complex visual stimuli.

Original languageEnglish
Pages (from-to)65-72
Number of pages8
JournalCurrent Opinion in Neurobiology
StatePublished - Apr 2019

Bibliographical note

Publisher Copyright:
© 2019 Elsevier Ltd

ASJC Scopus subject areas

  • General Neuroscience


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