ArticleFrontiers in human neuroscience2021
Why Firing Rate Distributions Are Important for Understanding Spinal Central Pattern Generators.
Article in Frontiers in human neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Spatial and network principles behind neural generation of locomotion.Nature communications · 2026Article
- Article
- The spinal premotor network driving scratching flexor and extensor alternation.bioRxiv : the preprint server for biology · 2025Article
- Cell-type-specific origins of locomotor rhythmicity at different speeds in larval zebrafish.eLife · 2024Article
- Logarithmically scaled, gamma distributed neuronal spiking.The Journal of physiology · 2023Review
- Viral strategies for targeting spinal neuronal subtypes in adult wild-type rodents.Scientific reports · 2022Article
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2 authors.
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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.
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