The Redemption of Noise: Inference with Neural Populations

Rodrigo Echeveste, Máté Lengyel*

*Corresponding author for this work

Research output: Contribution to journalShort surveypeer-review

Abstract (may include machine translation)

In 2006, Ma et al. (Nat. Neurosci. 1006;9:1432–1438) presented an elegant theory for how populations of neurons might represent uncertainty to perform Bayesian inference. Critically, according to this theory, neural variability is no longer a nuisance, but rather a vital part of how the brain encodes probability distributions and performs computations with them.

Original languageEnglish
Pages (from-to)767-770
Number of pages4
JournalTrends in Neurosciences
Volume41
Issue number11
DOIs
StatePublished - Nov 2018

Keywords

  • Bayesian inference
  • cortex
  • neural network
  • neural variability
  • perception
  • uncertainty

Fingerprint

Dive into the research topics of 'The Redemption of Noise: Inference with Neural Populations'. Together they form a unique fingerprint.

Cite this