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...

Full description

Bibliographic Details
Main Authors: Berg, R.W (Author), Lindén, H. (Author)
Format: Article
Language:English
Published: Frontiers Media S.A. 2021
Subjects:
Online Access:View Fulltext in Publisher
LEADER 02788nam a2200361Ia 4500
001 10.3389-fnhum.2021.719388
008 220427s2021 CNT 000 0 und d
020 |a 16625161 (ISSN) 
245 1 0 |a Why Firing Rate Distributions Are Important for Understanding Spinal Central Pattern Generators 
260 0 |b Frontiers Media S.A.  |c 2021 
856 |z View Fulltext in Publisher  |u https://doi.org/10.3389/fnhum.2021.719388 
520 3 |a 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. © Copyright © 2021 Lindén and Berg. 
650 0 4 |a article 
650 0 4 |a balanced network 
650 0 4 |a central pattern generation 
650 0 4 |a central pattern generator 
650 0 4 |a controlled study 
650 0 4 |a excitation 
650 0 4 |a firing rate 
650 0 4 |a firing rate distribution 
650 0 4 |a human 
650 0 4 |a human cell 
650 0 4 |a lognormal 
650 0 4 |a motor control 
650 0 4 |a motor control 
650 0 4 |a normal distribution 
650 0 4 |a population distribution 
650 0 4 |a recurrent inhibition 
650 0 4 |a spinal cord 
650 0 4 |a spinal cord 
700 1 |a Berg, R.W.  |e author 
700 1 |a Lindén, H.  |e author 
773 |t Frontiers in Human Neuroscience