Definition
Automated parameter search in small network central pattern generators (CPGs) involves the use of any methods other than manual (i.e., hand-tuning) to generate or tune sets of parameters that result in physiologically realistic neuronal models of the CPGs. Such methods include “brute-force” explorations of predefined parameter spaces, as well as various heuristics (e.g., multi-objective evolutionary algorithms) used to arrive at a single or more of viable model parameter combinations.
Detailed Description
Central pattern generators (CPGs) are neural networks that produce rhythmically patterned outputs, without relying on any sensory feedback (Hooper 2001). CPGs drive such critical rhythmic activity as breathing, chewing, swimming, walking, heartbeat control, etc. CPGs have been shown to produce rhythmic outputs akin to normal rhythmic activity patterns, even in isolation from other parts of the nervous system, which makes them popular physiological models. Furthermore, due...
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Smolinski, T.G. (2014). Automated Parameter Search in Small Network Central Pattern Generators. In: Jaeger, D., Jung, R. (eds) Encyclopedia of Computational Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7320-6_23-2
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DOI: https://doi.org/10.1007/978-1-4614-7320-6_23-2
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