Classic model of the Tritonia Swim CPG (Getting, 1989)

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Accession:93326
Classic model developed by Petter Getting of the 3-cell core CPG (DSI, C2, and VSI-B) mediating escape swimming in Tritonia diomedea. Cells use a hybrid integrate-and-fire scheme pioneered by Peter Getting. Each model cell is reconstructed from extensive physiological measurements to precisely mimic I-F curves, synaptic waveforms, and functional connectivity. **However, continued physiological measurements show that Getting may have inadvertently incorporated modulatory and or polysynaptic effects -- the properties of this model do *not* match physiological measurements in rested preparations.** This simulation reconstructs the Getting model as reported in: Getting (1989) 'Reconstruction of small neural networks' In Methods in Neural Modeling, 1st ed, p. 171-196. See also, an earlier version of this model reported in Getting (1983). Every attempt has been made to replicate the 1989 model as precisely as possible.
Reference:
1 . Getting PA (1989) Reconstruction of small neural networks. Methods in Neuronal Modeling: From Synapses to Networks., Koch C:Segev I, ed. pp.171
2 . Getting PA (1983) Mechanisms of pattern generation underlying swimming in Tritonia. II. Network reconstruction. J Neurophysiol 49:1017-35 [PubMed]
Citations  Citation Browser
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Tritonia;
Cell Type(s): Tritonia swim interneuron dorsal; Tritonia cerebral cell; Tritonia swim interneuron ventral;
Channel(s): I A;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Bursting; Oscillations; Invertebrate;
Implementer(s): Calin-Jageman, Robert [rcalinjageman at gsu dot edu];
Search NeuronDB for information about:  I A; 
: Shunt current, voltage dependent
: Bob Calin-Jageman 9/20/2002
: This model implements a voltage-dependent, non-inactivating shunt current
: as described by
: Getting, 1989 and utilized by Lieb and Frost, 1998.
: 
: 
: Created by Bob Calin-Jageman
: 
: Created 	9/20/2002
: Modified 	9/20/2002
:
: Mathcheck  -  9/31/2002
: Unitscheck -  9/31/2002 (though G not literal mS)
: 
: Explanation
: Shunt current is equal to G * m * h * (V-Erev)
:	G - weight
:	m - activation level
:	h - inactivation level - always 1
:	v - current membrane potential in mv
:	Erev - reversal potential for the current
:
: m, the activation level changes as dm/dt = (m-ss - m) / tau-m
: where m-ss is the steady-state activation determined by
: m-ss = 1/( 1 + e ^((v+b)/c) )
: 
: 
: References
: 	Getting, P.A. (1989) "Reconstruction of small neural networks" in 
: Methods in Neuronal Modeling: From Synapses to Networks (1st ed), Kock & Segev
: eds, MIT Press.

: 	Lieb JR & Frost WN (1997) "Realistic Simulation of the Aplysia Siphon
: Withdrawal Reflex Circuit: Roles of Circuit Elements in Producing Motor Output"
: p. 1249 */
: 

NEURON {
	POINT_PROCESS shunt
	NONSPECIFIC_CURRENT i
	RANGE G, erev, Bm, Cm, Tm, Bh, Ch, Th, mmax, hmax, vstart
	}

UNITS {
	(mS) = (microsiemens)
	(mV) = (millivolt)
	(nA) = (nanoamp)
}

PARAMETER  {
	G = .28 (microsiemens)
	erev = -56.9 (mV)
	Bm (1)
	Cm (1)
	Tm (1)
	Bh (1)
	Ch (1)
	Th (1)
	mmax (1)
	hmax (1)
	vstart (mv)
	}

ASSIGNED {
	i	(nA)
	v	(mV)
	}

STATE { m (1) h (1)}

BREAKPOINT {
	mmax = 1/(1+exp((v+Bm)/Cm))
	hmax = 1/(1+exp((v+Bh)/Ch))
	SOLVE state METHOD cnexp
	i = G * m * h * (v - erev)
	}	

INITIAL {
	m = 1/(1+exp((vstart+Bm)/Cm))
	h = 1/(1+exp((vstart+Bh)/Ch))
}

DERIVATIVE state {
	m' = (mmax - m)/Tm
	h' = (hmax - h)/Th
}