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A Dynamic Efficient Sensory Encoding Approach to Adaptive Tuning in Neural Models of Optic Flow Processing
This paper introduces a self-tuning mechanism for capturing rapid adaptation to changing visual stimuli by a population of neurons. Building upon the principles of efficient sensory encoding, we show how neural tuning curve parameters can be continually updated to optimally encode a time-varying dis...
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/PMC9011806/ https://www.ncbi.nlm.nih.gov/pubmed/35431848 http://dx.doi.org/10.3389/fncom.2022.844289 |
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author | Steinmetz, Scott T. Layton, Oliver W. Powell, Nathaniel V. Fajen, Brett R. |
author_facet | Steinmetz, Scott T. Layton, Oliver W. Powell, Nathaniel V. Fajen, Brett R. |
author_sort | Steinmetz, Scott T. |
collection | PubMed |
description | This paper introduces a self-tuning mechanism for capturing rapid adaptation to changing visual stimuli by a population of neurons. Building upon the principles of efficient sensory encoding, we show how neural tuning curve parameters can be continually updated to optimally encode a time-varying distribution of recently detected stimulus values. We implemented this mechanism in a neural model that produces human-like estimates of self-motion direction (i.e., heading) based on optic flow. The parameters of speed-sensitive units were dynamically tuned in accordance with efficient sensory encoding such that the network remained sensitive as the distribution of optic flow speeds varied. In two simulation experiments, we found that model performance with dynamic tuning yielded more accurate, shorter latency heading estimates compared to the model with static tuning. We conclude that dynamic efficient sensory encoding offers a plausible approach for capturing adaptation to varying visual environments in biological visual systems and neural models alike. |
format | Online Article Text |
id | pubmed-9011806 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90118062022-04-16 A Dynamic Efficient Sensory Encoding Approach to Adaptive Tuning in Neural Models of Optic Flow Processing Steinmetz, Scott T. Layton, Oliver W. Powell, Nathaniel V. Fajen, Brett R. Front Comput Neurosci Computational Neuroscience This paper introduces a self-tuning mechanism for capturing rapid adaptation to changing visual stimuli by a population of neurons. Building upon the principles of efficient sensory encoding, we show how neural tuning curve parameters can be continually updated to optimally encode a time-varying distribution of recently detected stimulus values. We implemented this mechanism in a neural model that produces human-like estimates of self-motion direction (i.e., heading) based on optic flow. The parameters of speed-sensitive units were dynamically tuned in accordance with efficient sensory encoding such that the network remained sensitive as the distribution of optic flow speeds varied. In two simulation experiments, we found that model performance with dynamic tuning yielded more accurate, shorter latency heading estimates compared to the model with static tuning. We conclude that dynamic efficient sensory encoding offers a plausible approach for capturing adaptation to varying visual environments in biological visual systems and neural models alike. Frontiers Media S.A. 2022-04-01 /pmc/articles/PMC9011806/ /pubmed/35431848 http://dx.doi.org/10.3389/fncom.2022.844289 Text en Copyright © 2022 Steinmetz, Layton, Powell and Fajen. 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 Steinmetz, Scott T. Layton, Oliver W. Powell, Nathaniel V. Fajen, Brett R. A Dynamic Efficient Sensory Encoding Approach to Adaptive Tuning in Neural Models of Optic Flow Processing |
title | A Dynamic Efficient Sensory Encoding Approach to Adaptive Tuning in Neural Models of Optic Flow Processing |
title_full | A Dynamic Efficient Sensory Encoding Approach to Adaptive Tuning in Neural Models of Optic Flow Processing |
title_fullStr | A Dynamic Efficient Sensory Encoding Approach to Adaptive Tuning in Neural Models of Optic Flow Processing |
title_full_unstemmed | A Dynamic Efficient Sensory Encoding Approach to Adaptive Tuning in Neural Models of Optic Flow Processing |
title_short | A Dynamic Efficient Sensory Encoding Approach to Adaptive Tuning in Neural Models of Optic Flow Processing |
title_sort | dynamic efficient sensory encoding approach to adaptive tuning in neural models of optic flow processing |
topic | Computational Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9011806/ https://www.ncbi.nlm.nih.gov/pubmed/35431848 http://dx.doi.org/10.3389/fncom.2022.844289 |
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