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Why Firing Rate Distributions Are Important for Understanding Spinal Central Pattern Generators
Networks in the spinal cord, which are responsible for the generation of rhythmic movements, commonly known as central pattern generators (CPGs), have remained elusive for decades. Although it is well-known that many spinal neurons are rhythmically active, little attention has been given to the dist...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8446347/ https://www.ncbi.nlm.nih.gov/pubmed/34539363 http://dx.doi.org/10.3389/fnhum.2021.719388 |
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author | Lindén, Henrik Berg, Rune W. |
author_facet | Lindén, Henrik Berg, Rune W. |
author_sort | Lindén, Henrik |
collection | PubMed |
description | Networks in the spinal cord, which are responsible for the generation of rhythmic movements, commonly known as central pattern generators (CPGs), have remained elusive for decades. Although it is well-known that many spinal neurons are rhythmically active, little attention has been given to the distribution of firing rates across the population. Here, we argue that firing rate distributions can provide an important clue to the organization of the CPGs. The data that can be gleaned from the sparse literature indicate a firing rate distribution, which is skewed toward zero with a long tail, akin to a normal distribution on a log-scale, i.e., a “log-normal” distribution. Importantly, such a shape is difficult to unite with the widespread assumption of modules composed of recurrently connected excitatory neurons. Spinal modules with recurrent excitation has the propensity to quickly escalate their firing rate and reach the maximum, hence equalizing the spiking activity across the population. The population distribution of firing rates hence would consist of a narrow peak near the maximum. This is incompatible with experiments, that show wide distributions and a peak close to zero. A way to resolve this puzzle is to include recurrent inhibition internally in each CPG modules. Hence, we investigate the impact of recurrent inhibition in a model and find that the firing rate distributions are closer to the experimentally observed. We therefore propose that recurrent inhibition is a crucial element in motor circuits, and suggest that future models of motor circuits should include recurrent inhibition as a mandatory element. |
format | Online Article Text |
id | pubmed-8446347 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-84463472021-09-18 Why Firing Rate Distributions Are Important for Understanding Spinal Central Pattern Generators Lindén, Henrik Berg, Rune W. Front Hum Neurosci Human Neuroscience Networks in the spinal cord, which are responsible for the generation of rhythmic movements, commonly known as central pattern generators (CPGs), have remained elusive for decades. Although it is well-known that many spinal neurons are rhythmically active, little attention has been given to the distribution of firing rates across the population. Here, we argue that firing rate distributions can provide an important clue to the organization of the CPGs. The data that can be gleaned from the sparse literature indicate a firing rate distribution, which is skewed toward zero with a long tail, akin to a normal distribution on a log-scale, i.e., a “log-normal” distribution. Importantly, such a shape is difficult to unite with the widespread assumption of modules composed of recurrently connected excitatory neurons. Spinal modules with recurrent excitation has the propensity to quickly escalate their firing rate and reach the maximum, hence equalizing the spiking activity across the population. The population distribution of firing rates hence would consist of a narrow peak near the maximum. This is incompatible with experiments, that show wide distributions and a peak close to zero. A way to resolve this puzzle is to include recurrent inhibition internally in each CPG modules. Hence, we investigate the impact of recurrent inhibition in a model and find that the firing rate distributions are closer to the experimentally observed. We therefore propose that recurrent inhibition is a crucial element in motor circuits, and suggest that future models of motor circuits should include recurrent inhibition as a mandatory element. Frontiers Media S.A. 2021-09-03 /pmc/articles/PMC8446347/ /pubmed/34539363 http://dx.doi.org/10.3389/fnhum.2021.719388 Text en Copyright © 2021 Lindén and Berg. 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 | Human Neuroscience Lindén, Henrik Berg, Rune W. Why Firing Rate Distributions Are Important for Understanding Spinal Central Pattern Generators |
title | Why Firing Rate Distributions Are Important for Understanding Spinal Central Pattern Generators |
title_full | Why Firing Rate Distributions Are Important for Understanding Spinal Central Pattern Generators |
title_fullStr | Why Firing Rate Distributions Are Important for Understanding Spinal Central Pattern Generators |
title_full_unstemmed | Why Firing Rate Distributions Are Important for Understanding Spinal Central Pattern Generators |
title_short | Why Firing Rate Distributions Are Important for Understanding Spinal Central Pattern Generators |
title_sort | why firing rate distributions are important for understanding spinal central pattern generators |
topic | Human Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8446347/ https://www.ncbi.nlm.nih.gov/pubmed/34539363 http://dx.doi.org/10.3389/fnhum.2021.719388 |
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