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Efficient Temporal Coding in the Early Visual System: Existing Evidence and Future Directions
While it is universally accepted that the brain makes predictions, there is little agreement about how this is accomplished and under which conditions. Accurate prediction requires neural circuits to learn and store spatiotemporal patterns observed in the natural environment, but it is not obvious h...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9298461/ https://www.ncbi.nlm.nih.gov/pubmed/35874317 http://dx.doi.org/10.3389/fncom.2022.929348 |
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author | Price, Byron H. Gavornik, Jeffrey P. |
author_facet | Price, Byron H. Gavornik, Jeffrey P. |
author_sort | Price, Byron H. |
collection | PubMed |
description | While it is universally accepted that the brain makes predictions, there is little agreement about how this is accomplished and under which conditions. Accurate prediction requires neural circuits to learn and store spatiotemporal patterns observed in the natural environment, but it is not obvious how such information should be stored, or encoded. Information theory provides a mathematical formalism that can be used to measure the efficiency and utility of different coding schemes for data transfer and storage. This theory shows that codes become efficient when they remove predictable, redundant spatial and temporal information. Efficient coding has been used to understand retinal computations and may also be relevant to understanding more complicated temporal processing in visual cortex. However, the literature on efficient coding in cortex is varied and can be confusing since the same terms are used to mean different things in different experimental and theoretical contexts. In this work, we attempt to provide a clear summary of the theoretical relationship between efficient coding and temporal prediction, and review evidence that efficient coding principles explain computations in the retina. We then apply the same framework to computations occurring in early visuocortical areas, arguing that data from rodents is largely consistent with the predictions of this model. Finally, we review and respond to criticisms of efficient coding and suggest ways that this theory might be used to design future experiments, with particular focus on understanding the extent to which neural circuits make predictions from efficient representations of environmental statistics. |
format | Online Article Text |
id | pubmed-9298461 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92984612022-07-21 Efficient Temporal Coding in the Early Visual System: Existing Evidence and Future Directions Price, Byron H. Gavornik, Jeffrey P. Front Comput Neurosci Computational Neuroscience While it is universally accepted that the brain makes predictions, there is little agreement about how this is accomplished and under which conditions. Accurate prediction requires neural circuits to learn and store spatiotemporal patterns observed in the natural environment, but it is not obvious how such information should be stored, or encoded. Information theory provides a mathematical formalism that can be used to measure the efficiency and utility of different coding schemes for data transfer and storage. This theory shows that codes become efficient when they remove predictable, redundant spatial and temporal information. Efficient coding has been used to understand retinal computations and may also be relevant to understanding more complicated temporal processing in visual cortex. However, the literature on efficient coding in cortex is varied and can be confusing since the same terms are used to mean different things in different experimental and theoretical contexts. In this work, we attempt to provide a clear summary of the theoretical relationship between efficient coding and temporal prediction, and review evidence that efficient coding principles explain computations in the retina. We then apply the same framework to computations occurring in early visuocortical areas, arguing that data from rodents is largely consistent with the predictions of this model. Finally, we review and respond to criticisms of efficient coding and suggest ways that this theory might be used to design future experiments, with particular focus on understanding the extent to which neural circuits make predictions from efficient representations of environmental statistics. Frontiers Media S.A. 2022-07-04 /pmc/articles/PMC9298461/ /pubmed/35874317 http://dx.doi.org/10.3389/fncom.2022.929348 Text en Copyright © 2022 Price and Gavornik. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Computational Neuroscience Price, Byron H. Gavornik, Jeffrey P. Efficient Temporal Coding in the Early Visual System: Existing Evidence and Future Directions |
title | Efficient Temporal Coding in the Early Visual System: Existing Evidence and Future Directions |
title_full | Efficient Temporal Coding in the Early Visual System: Existing Evidence and Future Directions |
title_fullStr | Efficient Temporal Coding in the Early Visual System: Existing Evidence and Future Directions |
title_full_unstemmed | Efficient Temporal Coding in the Early Visual System: Existing Evidence and Future Directions |
title_short | Efficient Temporal Coding in the Early Visual System: Existing Evidence and Future Directions |
title_sort | efficient temporal coding in the early visual system: existing evidence and future directions |
topic | Computational Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9298461/ https://www.ncbi.nlm.nih.gov/pubmed/35874317 http://dx.doi.org/10.3389/fncom.2022.929348 |
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