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Bayesian inference in ring attractor networks
Working memories are thought to be held in attractor networks in the brain. These attractors should keep track of the uncertainty associated with each memory, so as to weigh it properly against conflicting new evidence. However, conventional attractors do not represent uncertainty. Here, we show how...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9992764/ https://www.ncbi.nlm.nih.gov/pubmed/36812206 http://dx.doi.org/10.1073/pnas.2210622120 |
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author | Kutschireiter, Anna Basnak, Melanie A. Wilson, Rachel I. Drugowitsch, Jan |
author_facet | Kutschireiter, Anna Basnak, Melanie A. Wilson, Rachel I. Drugowitsch, Jan |
author_sort | Kutschireiter, Anna |
collection | PubMed |
description | Working memories are thought to be held in attractor networks in the brain. These attractors should keep track of the uncertainty associated with each memory, so as to weigh it properly against conflicting new evidence. However, conventional attractors do not represent uncertainty. Here, we show how uncertainty could be incorporated into an attractor, specifically a ring attractor that encodes head direction. First, we introduce a rigorous normative framework (the circular Kalman filter) for benchmarking the performance of a ring attractor under conditions of uncertainty. Next, we show that the recurrent connections within a conventional ring attractor can be retuned to match this benchmark. This allows the amplitude of network activity to grow in response to confirmatory evidence, while shrinking in response to poor-quality or strongly conflicting evidence. This “Bayesian ring attractor” performs near-optimal angular path integration and evidence accumulation. Indeed, we show that a Bayesian ring attractor is consistently more accurate than a conventional ring attractor. Moreover, near-optimal performance can be achieved without exact tuning of the network connections. Finally, we use large-scale connectome data to show that the network can achieve near-optimal performance even after we incorporate biological constraints. Our work demonstrates how attractors can implement a dynamic Bayesian inference algorithm in a biologically plausible manner, and it makes testable predictions with direct relevance to the head direction system as well as any neural system that tracks direction, orientation, or periodic rhythms. |
format | Online Article Text |
id | pubmed-9992764 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-99927642023-08-22 Bayesian inference in ring attractor networks Kutschireiter, Anna Basnak, Melanie A. Wilson, Rachel I. Drugowitsch, Jan Proc Natl Acad Sci U S A Biological Sciences Working memories are thought to be held in attractor networks in the brain. These attractors should keep track of the uncertainty associated with each memory, so as to weigh it properly against conflicting new evidence. However, conventional attractors do not represent uncertainty. Here, we show how uncertainty could be incorporated into an attractor, specifically a ring attractor that encodes head direction. First, we introduce a rigorous normative framework (the circular Kalman filter) for benchmarking the performance of a ring attractor under conditions of uncertainty. Next, we show that the recurrent connections within a conventional ring attractor can be retuned to match this benchmark. This allows the amplitude of network activity to grow in response to confirmatory evidence, while shrinking in response to poor-quality or strongly conflicting evidence. This “Bayesian ring attractor” performs near-optimal angular path integration and evidence accumulation. Indeed, we show that a Bayesian ring attractor is consistently more accurate than a conventional ring attractor. Moreover, near-optimal performance can be achieved without exact tuning of the network connections. Finally, we use large-scale connectome data to show that the network can achieve near-optimal performance even after we incorporate biological constraints. Our work demonstrates how attractors can implement a dynamic Bayesian inference algorithm in a biologically plausible manner, and it makes testable predictions with direct relevance to the head direction system as well as any neural system that tracks direction, orientation, or periodic rhythms. National Academy of Sciences 2023-02-22 2023-02-28 /pmc/articles/PMC9992764/ /pubmed/36812206 http://dx.doi.org/10.1073/pnas.2210622120 Text en Copyright © 2023 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Biological Sciences Kutschireiter, Anna Basnak, Melanie A. Wilson, Rachel I. Drugowitsch, Jan Bayesian inference in ring attractor networks |
title | Bayesian inference in ring attractor networks |
title_full | Bayesian inference in ring attractor networks |
title_fullStr | Bayesian inference in ring attractor networks |
title_full_unstemmed | Bayesian inference in ring attractor networks |
title_short | Bayesian inference in ring attractor networks |
title_sort | bayesian inference in ring attractor networks |
topic | Biological Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9992764/ https://www.ncbi.nlm.nih.gov/pubmed/36812206 http://dx.doi.org/10.1073/pnas.2210622120 |
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