Functional diversity among sensory neurons from efficient coding principles.
Journal
PLoS computational biology
ISSN: 1553-7358
Titre abrégé: PLoS Comput Biol
Pays: United States
ID NLM: 101238922
Informations de publication
Date de publication:
11 2019
11 2019
Historique:
received:
24
07
2018
accepted:
10
10
2019
revised:
03
12
2019
pubmed:
15
11
2019
medline:
15
2
2020
entrez:
15
11
2019
Statut:
epublish
Résumé
In many sensory systems the neural signal is coded by the coordinated response of heterogeneous populations of neurons. What computational benefit does this diversity confer on information processing? We derive an efficient coding framework assuming that neurons have evolved to communicate signals optimally given natural stimulus statistics and metabolic constraints. Incorporating nonlinearities and realistic noise, we study optimal population coding of the same sensory variable using two measures: maximizing the mutual information between stimuli and responses, and minimizing the error incurred by the optimal linear decoder of responses. Our theory is applied to a commonly observed splitting of sensory neurons into ON and OFF that signal stimulus increases or decreases, and to populations of monotonically increasing responses of the same type, ON. Depending on the optimality measure, we make different predictions about how to optimally split a population into ON and OFF, and how to allocate the firing thresholds of individual neurons given realistic stimulus distributions and noise, which accord with certain biases observed experimentally.
Identifiants
pubmed: 31725714
doi: 10.1371/journal.pcbi.1007476
pii: PCOMPBIOL-D-18-01282
pmc: PMC6890262
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e1007476Déclaration de conflit d'intérêts
The authors have declared that no competing interests exist.
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