ArticleNature communications2019
Decoupling of timescales reveals sparse convergent CPG network in the adult spinal cord.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Spatial and network principles behind neural generation of locomotion.Nature communications · 2026Article
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- Transition from predictable to variable motor cortex and striatal ensemble patterning during behavioral exploration.Nature communications · 2022Article
- Toward Assessing the Functional Connectivity of Spinal Neurons.Frontiers in neural circuits · 2022Article
- Modeling Post-Scratching Locomotion with Two Rhythm Generators and a Shared Pattern Formation.Biology · 2021Article
- Motoneurone synchronization for intercostal and abdominal muscles: interneurone influences in two different species.Experimental brain research · 2021Article
- Why Firing Rate Distributions Are Important for Understanding Spinal Central Pattern Generators.Frontiers in human neuroscience · 2021Article
- Neurotransmitters and Motoneuron Contacts of Multifunctional and Behaviorally Specialized Turtle Spinal Cord Interneurons.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2020Article
- Neural Interactions in Developing Rhythmogenic Spinal Networks: Insights From Computational Modeling.Frontiers in neural circuits · 2020Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
During the generation of rhythmic movements, most spinal neurons receive an oscillatory synaptic drive. The neuronal architecture underlying this drive is unknown, and the corresponding network size and sparseness have not yet been addressed. If the input originates from a small central pattern generator (CPG) with dense divergent connectivity, it will induce correlated input to all receiving neurons, while sparse convergent wiring will induce a weak correlation, if any. Here, we use pairwise recordings of spinal neurons to measure synaptic correlations and thus infer the wiring architecture qualitatively. A strong correlation on a slow timescale implies functional relatedness and a common source, which will also cause correlation on fast timescale due to shared synaptic connections. However, we consistently find marginal coupling between slow and fast correlations regardless of neuronal identity. This suggests either sparse convergent connectivity or a CPG network with recurrent inhibition that actively decorrelates common input.
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