Identification of Multiple Noise Sources Improves Estimation of Neural Responses across Stimulus Conditions.
Journal
eNeuro
ISSN: 2373-2822
Titre abrégé: eNeuro
Pays: United States
ID NLM: 101647362
Informations de publication
Date de publication:
Historique:
received:
11
02
2021
accepted:
10
05
2021
pubmed:
5
6
2021
medline:
14
8
2021
entrez:
4
6
2021
Statut:
epublish
Résumé
Most models of neural responses are constructed to reproduce the average response to inputs but lack the flexibility to capture observed variability in responses. The origins and structure of this variability have significant implications for how information is encoded and processed in the nervous system, both by limiting information that can be conveyed and by determining processing strategies that are favorable for minimizing its negative effects. Here, we present a new modeling framework that incorporates multiple sources of noise to better capture observed features of neural response variability across stimulus conditions. We apply this model to retinal ganglion cells at two different ambient light levels and demonstrate that it captures the full distribution of responses. Further, the model reveals light level-dependent changes that could not be seen with previous models, showing both large changes in rectification of nonlinear circuit elements and systematic differences in the contributions of different noise sources under different conditions.
Identifiants
pubmed: 34083382
pii: ENEURO.0191-21.2021
doi: 10.1523/ENEURO.0191-21.2021
pmc: PMC8260275
pii:
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : NEI NIH HHS
ID : R01 EY028111
Pays : United States
Organisme : NEI NIH HHS
ID : R01 EY028542
Pays : United States
Informations de copyright
Copyright © 2021 Weber et al.
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