Cargando…

Multiple bumps can enhance robustness to noise in continuous attractor networks

A central function of continuous attractor networks is encoding coordinates and accurately updating their values through path integration. To do so, these networks produce localized bumps of activity that move coherently in response to velocity inputs. In the brain, continuous attractors are believe...

Descripción completa

Detalles Bibliográficos
Autores principales: Wang, Raymond, Kang, Louis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9584540/
https://www.ncbi.nlm.nih.gov/pubmed/36215305
http://dx.doi.org/10.1371/journal.pcbi.1010547
_version_ 1784813290361192448
author Wang, Raymond
Kang, Louis
author_facet Wang, Raymond
Kang, Louis
author_sort Wang, Raymond
collection PubMed
description A central function of continuous attractor networks is encoding coordinates and accurately updating their values through path integration. To do so, these networks produce localized bumps of activity that move coherently in response to velocity inputs. In the brain, continuous attractors are believed to underlie grid cells and head direction cells, which maintain periodic representations of position and orientation, respectively. These representations can be achieved with any number of activity bumps, and the consequences of having more or fewer bumps are unclear. We address this knowledge gap by constructing 1D ring attractor networks with different bump numbers and characterizing their responses to three types of noise: fluctuating inputs, spiking noise, and deviations in connectivity away from ideal attractor configurations. Across all three types, networks with more bumps experience less noise-driven deviations in bump motion. This translates to more robust encodings of linear coordinates, like position, assuming that each neuron represents a fixed length no matter the bump number. Alternatively, we consider encoding a circular coordinate, like orientation, such that the network distance between adjacent bumps always maps onto 360 degrees. Under this mapping, bump number does not significantly affect the amount of error in the coordinate readout. Our simulation results are intuitively explained and quantitatively matched by a unified theory for path integration and noise in multi-bump networks. Thus, to suppress the effects of biologically relevant noise, continuous attractor networks can employ more bumps when encoding linear coordinates; this advantage disappears when encoding circular coordinates. Our findings provide motivation for multiple bumps in the mammalian grid network.
format Online
Article
Text
id pubmed-9584540
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-95845402022-10-21 Multiple bumps can enhance robustness to noise in continuous attractor networks Wang, Raymond Kang, Louis PLoS Comput Biol Research Article A central function of continuous attractor networks is encoding coordinates and accurately updating their values through path integration. To do so, these networks produce localized bumps of activity that move coherently in response to velocity inputs. In the brain, continuous attractors are believed to underlie grid cells and head direction cells, which maintain periodic representations of position and orientation, respectively. These representations can be achieved with any number of activity bumps, and the consequences of having more or fewer bumps are unclear. We address this knowledge gap by constructing 1D ring attractor networks with different bump numbers and characterizing their responses to three types of noise: fluctuating inputs, spiking noise, and deviations in connectivity away from ideal attractor configurations. Across all three types, networks with more bumps experience less noise-driven deviations in bump motion. This translates to more robust encodings of linear coordinates, like position, assuming that each neuron represents a fixed length no matter the bump number. Alternatively, we consider encoding a circular coordinate, like orientation, such that the network distance between adjacent bumps always maps onto 360 degrees. Under this mapping, bump number does not significantly affect the amount of error in the coordinate readout. Our simulation results are intuitively explained and quantitatively matched by a unified theory for path integration and noise in multi-bump networks. Thus, to suppress the effects of biologically relevant noise, continuous attractor networks can employ more bumps when encoding linear coordinates; this advantage disappears when encoding circular coordinates. Our findings provide motivation for multiple bumps in the mammalian grid network. Public Library of Science 2022-10-10 /pmc/articles/PMC9584540/ /pubmed/36215305 http://dx.doi.org/10.1371/journal.pcbi.1010547 Text en © 2022 Wang, Kang https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Wang, Raymond
Kang, Louis
Multiple bumps can enhance robustness to noise in continuous attractor networks
title Multiple bumps can enhance robustness to noise in continuous attractor networks
title_full Multiple bumps can enhance robustness to noise in continuous attractor networks
title_fullStr Multiple bumps can enhance robustness to noise in continuous attractor networks
title_full_unstemmed Multiple bumps can enhance robustness to noise in continuous attractor networks
title_short Multiple bumps can enhance robustness to noise in continuous attractor networks
title_sort multiple bumps can enhance robustness to noise in continuous attractor networks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9584540/
https://www.ncbi.nlm.nih.gov/pubmed/36215305
http://dx.doi.org/10.1371/journal.pcbi.1010547
work_keys_str_mv AT wangraymond multiplebumpscanenhancerobustnesstonoiseincontinuousattractornetworks
AT kanglouis multiplebumpscanenhancerobustnesstonoiseincontinuousattractornetworks