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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References
Doloc-Mihu A, Calabrese R (2011) A database of computational models of a half-center oscillator for analyzing how neuronal parameters influence network activity. J Biol Phys 37:263–283
Günay C, Prinz AA (2010) Model calcium sensors for network homeostasis: sensor and readout parameter analysis from a database of model neuronal networks. J Neurosci 30(5):1686–169
Hooper SL (2001) Central pattern generators. In: Encyclopedia of life sciences. Wiley Hoboken, NJ
Malik A, Shim K, Prinz AA, Smolinski TG (2013) Multi-objective evolutionary algorithms for analysis of conductance correlations involved in recovery of bursting after neuromodulator deprivation in lobster stomatogastric neuron models. BMC Neurosci 14(1):P370
Shim K, Prinz AA, Smolinski TG (2012) Analyzing conductance correlations involved in recovery of bursting after neuromodulator deprivation in lobster stomatogastric neuron models. BMC Neurosci 13(1):P37
Smolinski TG, Prinz AA (2009) Computational intelligence in modeling of biological neurons: A case study of an invertebrate pacemaker neuron. In: Proceedings of international joint conference on neural networks, Atlanta, GA, pp 2964–2970
Smolinski TG, Soto-Treviño C, Rabbah P, Nadim F, Prinz AA (2006) Analysis of biological neurons via modeling and rule mining. Int J Inf Technol Intell Comput 1(2):293–302
Soto-Treviño C, Rabbah P, Marder E, Nadim F (2005) Computational model of electrically coupled, intrinsically distinct pacemaker neurons. J Neurophysiol 94(2):590–604
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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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