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Identification of Multiple Noise Sources Improves Estimation of Neural Responses across Stimulus Conditions
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 s...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Society for Neuroscience
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8260275/ https://www.ncbi.nlm.nih.gov/pubmed/34083382 http://dx.doi.org/10.1523/ENEURO.0191-21.2021 |
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author | Weber, Alison I. Shea-Brown, Eric Rieke, Fred |
author_facet | Weber, Alison I. Shea-Brown, Eric Rieke, Fred |
author_sort | Weber, Alison I. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8260275 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Society for Neuroscience |
record_format | MEDLINE/PubMed |
spelling | pubmed-82602752021-07-08 Identification of Multiple Noise Sources Improves Estimation of Neural Responses across Stimulus Conditions Weber, Alison I. Shea-Brown, Eric Rieke, Fred eNeuro Research Article: New Research 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. Society for Neuroscience 2021-07-03 /pmc/articles/PMC8260275/ /pubmed/34083382 http://dx.doi.org/10.1523/ENEURO.0191-21.2021 Text en Copyright © 2021 Weber et al. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed. |
spellingShingle | Research Article: New Research Weber, Alison I. Shea-Brown, Eric Rieke, Fred Identification of Multiple Noise Sources Improves Estimation of Neural Responses across Stimulus Conditions |
title | Identification of Multiple Noise Sources Improves Estimation of Neural Responses across Stimulus Conditions |
title_full | Identification of Multiple Noise Sources Improves Estimation of Neural Responses across Stimulus Conditions |
title_fullStr | Identification of Multiple Noise Sources Improves Estimation of Neural Responses across Stimulus Conditions |
title_full_unstemmed | Identification of Multiple Noise Sources Improves Estimation of Neural Responses across Stimulus Conditions |
title_short | Identification of Multiple Noise Sources Improves Estimation of Neural Responses across Stimulus Conditions |
title_sort | identification of multiple noise sources improves estimation of neural responses across stimulus conditions |
topic | Research Article: New Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8260275/ https://www.ncbi.nlm.nih.gov/pubmed/34083382 http://dx.doi.org/10.1523/ENEURO.0191-21.2021 |
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