Efficiency turns the table on neural encoding, decoding and noise.

Abstract:

:Sensory neurons are usually described with an encoding model, for example, a function that predicts their response from the sensory stimulus using a receptive field (RF) or a tuning curve. However, central to theories of sensory processing is the notion of 'efficient coding'. We argue here that efficient coding implies a completely different neural coding strategy. Instead of a fixed encoding model, neural populations would be described by a fixed decoding model (i.e. a model reconstructing the stimulus from the neural responses). Because the population solves a global optimization problem, individual neurons are variable, but not noisy, and have no truly invariant tuning curve or receptive field. We review recent experimental evidence and implications for neural noise correlations, robustness and adaptation.

journal_name

Curr Opin Neurobiol

authors

Deneve S,Chalk M

doi

10.1016/j.conb.2016.03.002

subject

Has Abstract

pub_date

2016-04-01 00:00:00

pages

141-148

eissn

0959-4388

issn

1873-6882

pii

S0959-4388(16)30011-3

journal_volume

37

pub_type

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