Marginalization in neural circuits with divisive normalization.

Journal Article (Journal Article)

A wide range of computations performed by the nervous system involves a type of probabilistic inference known as marginalization. This computation comes up in seemingly unrelated tasks, including causal reasoning, odor recognition, motor control, visual tracking, coordinate transformations, visual search, decision making, and object recognition, to name just a few. The question we address here is: how could neural circuits implement such marginalizations? We show that when spike trains exhibit a particular type of statistics--associated with constant Fano factors and gain-invariant tuning curves, as is often reported in vivo--some of the more common marginalizations can be achieved with networks that implement a quadratic nonlinearity and divisive normalization, the latter being a type of nonlinear lateral inhibition that has been widely reported in neural circuits. Previous studies have implicated divisive normalization in contrast gain control and attentional modulation. Our results raise the possibility that it is involved in yet another, highly critical, computation: near optimal marginalization in a remarkably wide range of tasks.

Full Text

Duke Authors

Cited Authors

  • Beck, JM; Latham, PE; Pouget, A

Published Date

  • October 26, 2011

Published In

Volume / Issue

  • 31 / 43

Start / End Page

  • 15310 - 15319

PubMed ID

  • 22031877

Pubmed Central ID

  • PMC3230133

Electronic International Standard Serial Number (EISSN)

  • 1529-2401

Digital Object Identifier (DOI)

  • 10.1523/JNEUROSCI.1706-11.2011


  • eng

Conference Location

  • United States