Structural and functional properties of a probabilistic model of neuronal connectivity in a simple locomotor network
- University of Plymouth, United Kingdom
- University of Bristol, United Kingdom
- Cited 5
- Views 1,244
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Abstract
Although, in most animals, brain connectivity varies between individuals, behaviour is often similar across a species. What fundamental structural properties are shared across individual networks that define this behaviour? We describe a probabilistic model of connectivity in the hatchling Xenopus tadpole spinal cord which, when combined with a spiking model, reliably produces rhythmic activity corresponding to swimming. The probabilistic model allows calculation of structural characteristics that reflect common network properties, independent of individual network realisations. We use the structural characteristics to study examples of neuronal dynamics, in the complete network and various sub-networks, and this allows us to explain the basis for key experimental findings, and make predictions for experiments. We also study how structural and functional features differ between detailed anatomical connectomes and those generated by our new, simpler, model.
Article and author information
Author details
Funding
Biotechnology and Biological Sciences Research Council (BB/L000814/1)
- Andrea Ferrario
- Roman Borisyuk
Plymouth University (BB/L002353/1)
- Stephen R Soffe
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Ronald L Calabrese, Emory University, United States
Publication history
- Received: November 2, 2017
- Accepted: March 25, 2018
- Accepted Manuscript published: March 28, 2018 (version 1)
- Version of Record published: April 20, 2018 (version 2)
Copyright
© 2018, Ferrario et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Further reading
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- Neuroscience
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- Neuroscience
