Computational Model of a Central Pattern Generator (Cataldo et al 2006)

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Accession:65412
The buccal ganglia of Aplysia contain a central pattern generator (CPG) that mediates rhythmic movements of the foregut during feeding. This CPG is a multifunctional circuit and generates at least two types of buccal motor patterns (BMPs), one that mediates ingestion (iBMP) and another that mediates rejection (rBMP). The present study used a computational approach to examine the ways in which an ensemble of identified cells and synaptic connections function as a CPG. Hodgkin-Huxley-type models were developed that mimicked the biophysical properties of these cells and synaptic connections. The results suggest that the currently identified ensemble of cells is inadequate to produce rhythmic neural activity and that several key elements of the CPG remain to be identified.
Reference:
1 . Cataldo E, Byrne JH, Baxter DA (2006) Computational Model of a Central Pattern Generator CMSB 2006, Lecture Notes in Bioinformatics LNBI 4210, Priami C, ed. pp.242
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Aplysia;
Cell Type(s): Aplysia feeding CPG neurons;
Channel(s): I Chloride; I Na,p; I K; I Sodium; I Potassium;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: SNNAP;
Model Concept(s): Temporal Pattern Generation; Oscillations; Invertebrate;
Implementer(s):
Search NeuronDB for information about:  I Chloride; I Na,p; I K; I Sodium; I Potassium; 
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Ivd:		> 	Current due to a voltage-dependent conductance	>
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				>		p			>
	1			>	G= g x A x B 		(1)	>
   /B63/B63_Na.A	>A<	>					>
   /B63/B63_Na.B	>B<	>					>
	7.5 		>7.5 g<	>					>
	3 		>3 P<	>	Ivd = G x (V -E)		>
	50.0 		>50 E<	>					>
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>------------------------------->--------------------------------------->
>				>		p			>
>	2			>	Ivd= g x m x h 		(2)	>
>	model.m		>m<	>					>
>	model.h		>h<	>					>
>	0.012 		>g<	>					>
>	1 		>P<	>	Ivd = G x (V -E)		>
>	35 		>E<	>					>
>				>					>
>------------------------------->--------------------------------------->
>				>		p			>
>	3			>	G= g x A		(3)	>
>	model.A		>A<	>					>
>	0.012 		>g<	>					>
>	1 		>P<	>	Ivd = G x (V -E)		>
>	-72 		>E<	>					>
>				>					>
>------------------------------->--------------------------------------->
>				>		p			>
>	4			>	Ivd= g x m 		(4)	>
>	model.m		>m<	>					>
>	0.012 		>g<	>					>
>	1 		>P<	>	Ivd = G x (V -E)		>
>	35 		>E<	>					>
>				>					>
>------------------------------->--------------------------------------->
>				>					>
>	5			>	Ivd = G x (V -E)	(5)	>
>	0.012 		>g<	>					>
>	-72 		>E<	>					>
>				>					>
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