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Neural population geometry: An approach for understanding biological and artificial neural networks

Advances in experimental neuroscience have transformed our ability to explore the structure and function of neural circuits. At the same time, advances in machine learning have unleashed the remarkable computational power of artificial neural networks (ANNs). While these two fields have different to...

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Detalles Bibliográficos
Autores principales: Chung, SueYeon, Abbott, L. F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10695674/
https://www.ncbi.nlm.nih.gov/pubmed/34801787
http://dx.doi.org/10.1016/j.conb.2021.10.010
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author Chung, SueYeon
Abbott, L. F.
author_facet Chung, SueYeon
Abbott, L. F.
author_sort Chung, SueYeon
collection PubMed
description Advances in experimental neuroscience have transformed our ability to explore the structure and function of neural circuits. At the same time, advances in machine learning have unleashed the remarkable computational power of artificial neural networks (ANNs). While these two fields have different tools and applications, they present a similar challenge: namely, understanding how information is embedded and processed through high-dimensional representations to solve complex tasks. One approach to addressing this challenge is to utilize mathematical and computational tools to analyze the geometry of these high-dimensional representations, i.e., neural population geometry. We review examples of geometrical approaches providing insight into the function of biological and artificial neural networks: representation untangling in perception, a geometric theory of classification capacity, disentanglement, and abstraction in cognitive systems, topological representations underlying cognitive maps, dynamic untangling in motor systems, and a dynamical approach to cognition. Together, these findings illustrate an exciting trend at the intersection of machine learning, neuroscience, and geometry, in which neural population geometry provides a useful population-level mechanistic descriptor underlying task implementation. Importantly, geometric descriptions are applicable across sensory modalities, brain regions, network architectures, and timescales. Thus, neural population geometry has the potential to unify our understanding of structure and function in biological and artificial neural networks, bridging the gap between single neurons, population activities, and behavior.
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spelling pubmed-106956742023-12-04 Neural population geometry: An approach for understanding biological and artificial neural networks Chung, SueYeon Abbott, L. F. Curr Opin Neurobiol Article Advances in experimental neuroscience have transformed our ability to explore the structure and function of neural circuits. At the same time, advances in machine learning have unleashed the remarkable computational power of artificial neural networks (ANNs). While these two fields have different tools and applications, they present a similar challenge: namely, understanding how information is embedded and processed through high-dimensional representations to solve complex tasks. One approach to addressing this challenge is to utilize mathematical and computational tools to analyze the geometry of these high-dimensional representations, i.e., neural population geometry. We review examples of geometrical approaches providing insight into the function of biological and artificial neural networks: representation untangling in perception, a geometric theory of classification capacity, disentanglement, and abstraction in cognitive systems, topological representations underlying cognitive maps, dynamic untangling in motor systems, and a dynamical approach to cognition. Together, these findings illustrate an exciting trend at the intersection of machine learning, neuroscience, and geometry, in which neural population geometry provides a useful population-level mechanistic descriptor underlying task implementation. Importantly, geometric descriptions are applicable across sensory modalities, brain regions, network architectures, and timescales. Thus, neural population geometry has the potential to unify our understanding of structure and function in biological and artificial neural networks, bridging the gap between single neurons, population activities, and behavior. 2021-10 2021-11-19 /pmc/articles/PMC10695674/ /pubmed/34801787 http://dx.doi.org/10.1016/j.conb.2021.10.010 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) ).
spellingShingle Article
Chung, SueYeon
Abbott, L. F.
Neural population geometry: An approach for understanding biological and artificial neural networks
title Neural population geometry: An approach for understanding biological and artificial neural networks
title_full Neural population geometry: An approach for understanding biological and artificial neural networks
title_fullStr Neural population geometry: An approach for understanding biological and artificial neural networks
title_full_unstemmed Neural population geometry: An approach for understanding biological and artificial neural networks
title_short Neural population geometry: An approach for understanding biological and artificial neural networks
title_sort neural population geometry: an approach for understanding biological and artificial neural networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10695674/
https://www.ncbi.nlm.nih.gov/pubmed/34801787
http://dx.doi.org/10.1016/j.conb.2021.10.010
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