Why Firing Rate Distributions Are Important for Understanding Spinal Central Pattern Generators

Research output: Contribution to journalJournal articleResearchpeer-review

Standard

Why Firing Rate Distributions Are Important for Understanding Spinal Central Pattern Generators. / Lindén, Henrik; Berg, Rune W.

In: Frontiers in Human Neuroscience, Vol. 15, 719388, 2021.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Lindén, H & Berg, RW 2021, 'Why Firing Rate Distributions Are Important for Understanding Spinal Central Pattern Generators', Frontiers in Human Neuroscience, vol. 15, 719388. https://doi.org/10.3389/fnhum.2021.719388

APA

Lindén, H., & Berg, R. W. (2021). Why Firing Rate Distributions Are Important for Understanding Spinal Central Pattern Generators. Frontiers in Human Neuroscience, 15, [719388]. https://doi.org/10.3389/fnhum.2021.719388

Vancouver

Lindén H, Berg RW. Why Firing Rate Distributions Are Important for Understanding Spinal Central Pattern Generators. Frontiers in Human Neuroscience. 2021;15. 719388. https://doi.org/10.3389/fnhum.2021.719388

Author

Lindén, Henrik ; Berg, Rune W. / Why Firing Rate Distributions Are Important for Understanding Spinal Central Pattern Generators. In: Frontiers in Human Neuroscience. 2021 ; Vol. 15.

Bibtex

@article{8ef7d75ee06f4dc19958275ccc2c88dd,
title = "Why Firing Rate Distributions Are Important for Understanding Spinal Central Pattern Generators",
abstract = "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.",
keywords = "balanced network, central pattern generation, firing rate distribution, lognormal, motor control, spinal cord",
author = "Henrik Lind{\'e}n and Berg, {Rune W.}",
note = "Publisher Copyright: {\textcopyright} Copyright {\textcopyright} 2021 Lind{\'e}n and Berg.",
year = "2021",
doi = "10.3389/fnhum.2021.719388",
language = "English",
volume = "15",
journal = "Frontiers in Human Neuroscience",
issn = "1662-5161",
publisher = "Frontiers Research Foundation",

}

RIS

TY - JOUR

T1 - Why Firing Rate Distributions Are Important for Understanding Spinal Central Pattern Generators

AU - Lindén, Henrik

AU - Berg, Rune W.

N1 - Publisher Copyright: © Copyright © 2021 Lindén and Berg.

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - balanced network

KW - central pattern generation

KW - firing rate distribution

KW - lognormal

KW - motor control

KW - spinal cord

U2 - 10.3389/fnhum.2021.719388

DO - 10.3389/fnhum.2021.719388

M3 - Journal article

C2 - 34539363

AN - SCOPUS:85115204481

VL - 15

JO - Frontiers in Human Neuroscience

JF - Frontiers in Human Neuroscience

SN - 1662-5161

M1 - 719388

ER -

ID: 280896923