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Characteristic columnar connectivity caters to cortical computation: Replication, simulation, and evaluation of a microcircuit model
The neocortex, and with it the mammalian brain, achieves a level of computational efficiency like no other existing computational engine. A deeper understanding of its building blocks (cortical microcircuits), and their underlying computational principles is thus of paramount interest. To this end,...
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/PMC9615567/ https://www.ncbi.nlm.nih.gov/pubmed/36310713 http://dx.doi.org/10.3389/fnint.2022.923468 |
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author | Schulte to Brinke, Tobias Duarte, Renato Morrison, Abigail |
author_facet | Schulte to Brinke, Tobias Duarte, Renato Morrison, Abigail |
author_sort | Schulte to Brinke, Tobias |
collection | PubMed |
description | The neocortex, and with it the mammalian brain, achieves a level of computational efficiency like no other existing computational engine. A deeper understanding of its building blocks (cortical microcircuits), and their underlying computational principles is thus of paramount interest. To this end, we need reproducible computational models that can be analyzed, modified, extended and quantitatively compared. In this study, we further that aim by providing a replication of a seminal cortical column model. This model consists of noisy Hodgkin-Huxley neurons connected by dynamic synapses, whose connectivity scheme is based on empirical findings from intracellular recordings. Our analysis confirms the key original finding that the specific, data-based connectivity structure enhances the computational performance compared to a variety of alternatively structured control circuits. For this comparison, we use tasks based on spike patterns and rates that require the systems not only to have simple classification capabilities, but also to retain information over time and to be able to compute nonlinear functions. Going beyond the scope of the original study, we demonstrate that this finding is independent of the complexity of the neuron model, which further strengthens the argument that it is the connectivity which is crucial. Finally, a detailed analysis of the memory capabilities of the circuits reveals a stereotypical memory profile common across all circuit variants. Notably, the circuit with laminar structure does not retain stimulus any longer than any other circuit type. We therefore conclude that the model's computational advantage lies in a sharper representation of the stimuli. |
format | Online Article Text |
id | pubmed-9615567 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-96155672022-10-29 Characteristic columnar connectivity caters to cortical computation: Replication, simulation, and evaluation of a microcircuit model Schulte to Brinke, Tobias Duarte, Renato Morrison, Abigail Front Integr Neurosci Neuroscience The neocortex, and with it the mammalian brain, achieves a level of computational efficiency like no other existing computational engine. A deeper understanding of its building blocks (cortical microcircuits), and their underlying computational principles is thus of paramount interest. To this end, we need reproducible computational models that can be analyzed, modified, extended and quantitatively compared. In this study, we further that aim by providing a replication of a seminal cortical column model. This model consists of noisy Hodgkin-Huxley neurons connected by dynamic synapses, whose connectivity scheme is based on empirical findings from intracellular recordings. Our analysis confirms the key original finding that the specific, data-based connectivity structure enhances the computational performance compared to a variety of alternatively structured control circuits. For this comparison, we use tasks based on spike patterns and rates that require the systems not only to have simple classification capabilities, but also to retain information over time and to be able to compute nonlinear functions. Going beyond the scope of the original study, we demonstrate that this finding is independent of the complexity of the neuron model, which further strengthens the argument that it is the connectivity which is crucial. Finally, a detailed analysis of the memory capabilities of the circuits reveals a stereotypical memory profile common across all circuit variants. Notably, the circuit with laminar structure does not retain stimulus any longer than any other circuit type. We therefore conclude that the model's computational advantage lies in a sharper representation of the stimuli. Frontiers Media S.A. 2022-10-03 /pmc/articles/PMC9615567/ /pubmed/36310713 http://dx.doi.org/10.3389/fnint.2022.923468 Text en Copyright © 2022 Schulte to Brinke, Duarte and Morrison. 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 | Neuroscience Schulte to Brinke, Tobias Duarte, Renato Morrison, Abigail Characteristic columnar connectivity caters to cortical computation: Replication, simulation, and evaluation of a microcircuit model |
title | Characteristic columnar connectivity caters to cortical computation: Replication, simulation, and evaluation of a microcircuit model |
title_full | Characteristic columnar connectivity caters to cortical computation: Replication, simulation, and evaluation of a microcircuit model |
title_fullStr | Characteristic columnar connectivity caters to cortical computation: Replication, simulation, and evaluation of a microcircuit model |
title_full_unstemmed | Characteristic columnar connectivity caters to cortical computation: Replication, simulation, and evaluation of a microcircuit model |
title_short | Characteristic columnar connectivity caters to cortical computation: Replication, simulation, and evaluation of a microcircuit model |
title_sort | characteristic columnar connectivity caters to cortical computation: replication, simulation, and evaluation of a microcircuit model |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9615567/ https://www.ncbi.nlm.nih.gov/pubmed/36310713 http://dx.doi.org/10.3389/fnint.2022.923468 |
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