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Published on: March 18, 2019
Constancy and variability in the output of a central pattern generator.
Brian J Norris1, Angela Wenning, Terrence Michael Wright
1Department of Biology, Emory University, Atlanta, Georgia 30322, USA.
Summary
Biological variability in neural networks is common, but functional output remains stable. Compensatory changes in synaptic strength and timing allow each animal to achieve a unique network solution for consistent motor control.
Area of Science:
- Neuroscience
- Computational Biology
- Systems Biology
Background:
- Biological systems exhibit significant parameter variability across individuals.
- Central pattern generators (CPGs) maintain functional output despite intrinsic and synaptic variations.
- Compensatory changes in neural parameters are hypothesized to explain this functional robustness.
Purpose of the Study:
- To investigate if maintained relative synaptic strengths explain functional output consistency in the leech heartbeat CPG.
- To test the hypothesis that compensatory changes in synaptic parameters maintain network function across individual animals.
Main Methods:
- Utilized the leech heartbeat central pattern generator (CPG) model.
- Selected three segmental motor neurons receiving input from four premotor interneurons.
- Analyzed variations in absolute and relative synaptic strengths and input firing phases across individual leeches.
Main Results:
- Functional output (phase progression) was maintained across animals despite significant animal-to-animal variation in absolute synaptic strengths.
- Relative synaptic strengths onto individual motor neurons were not strictly maintained across animals.
- Variations in relative synaptic strengths did not strongly correlate with output phase, and input firing phases also varied considerably.
Conclusions:
- The number of inputs and temporal diversity of CPG network activity buffer against individual input variations.
- Each animal's neural network may achieve functional output through unique combinations of synaptic and temporal parameters.
- Robustness of biological networks arises from distributed control and compensatory mechanisms rather than strict parameter invariance.
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