Scientific article
Open access

Marginalization in neural circuits with divisive normalization

Published inThe Journal of neuroscience, vol. 31, no. 43, p. 15310-15319
Publication date2011

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.

  • Action Potentials/physiology
  • Computer Simulation
  • Humans
  • Models, Neurological
  • Nerve Net/physiology
  • Neurons/physiology
  • Normal Distribution
  • Probability
Citation (ISO format)
BECK, Jeffrey M, LATHAM, Peter E, POUGET, Alexandre. Marginalization in neural circuits with divisive normalization. In: The Journal of neuroscience, 2011, vol. 31, n° 43, p. 15310–15319. doi: 10.1523/JNEUROSCI.1706-11.2011
Main files (1)
Article (Published version)
ISSN of the journal0270-6474

Technical informations

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