STD-dependent and independent encoding of Input irregularity as spike rate (Luthman et al. 2011)

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Accession:144523
"... We use a conductance-based model of a CN neuron to study the effect of the regularity of Purkinje cell spiking on CN neuron activity. We find that increasing the irregularity of Purkinje cell activity accelerates the CN neuron spike rate and that the mechanism of this recoding of input irregularity as output spike rate depends on the number of Purkinje cells converging onto a CN neuron. ..."
Reference:
1 . Luthman J, Hoebeek FE, Maex R, Davey N, Adams R, De Zeeuw CI, Steuber V (2011) STD-dependent and independent encoding of input irregularity as spike rate in a computational model of a cerebellar nucleus neuron. Cerebellum 10:667-82 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s): Cerebellum deep nucleus neuron;
Channel(s): I Na,p; I Na,t; I L high threshold; I T low threshold; I K; I h; I K,Ca;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Temporal Pattern Generation; Short-term Synaptic Plasticity;
Implementer(s): Luthman, Johannes [jwluthman at gmail.com];
Search NeuronDB for information about:  I Na,p; I Na,t; I L high threshold; I T low threshold; I K; I h; I K,Ca;
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LuthmanEtAl2011
readme.txt
CaConc.mod *
CaHVA.mod *
CalConc.mod *
CaLVA.mod *
DCNsyn.mod *
DCNsynGABA.mod *
DCNsynNMDA.mod *
fKdr.mod *
GammaStim.mod *
h.mod *
NaF.mod *
NaP.mod *
pasDCN.mod *
SK.mod *
sKdr.mod *
TNC.mod *
DCN_mechs.hoc
DCN_morph.hoc *
DCN_recording.hoc
DCN_run.hoc
DCN_simulation.hoc
mosinit.hoc
OutputDCN_soma_1s_ap.dat
OutputDCN_soma_1s_time.dat
OutputDCN_soma_1s_trace.dat
                            
COMMENT by Johannes Luthman: 
Based on NEURON 6.0's built-in exp2syn.mod.
Changes made to the original: 
* tau1 renamed tauRise; tau2, tauFall
* restructuring of NEURON block
* microsiemens changed to siemens for consistency with the other NMODLs.


Original comment: 
Two state kinetic scheme synapse described by rise time tauRise,
and decay time constant tauFall. The normalized peak condunductance is 1.
Decay time MUST be greater than rise time.

The solution of A->G->bath with rate constants 1/tauRise and 1/tauFall is
 A = a*exp(-t/tauRise) and
 G = a*tauFall/(tauFall-tauRise)*(-exp(-t/tauRise) + exp(-t/tauFall))
	where tauRise < tauFall

If tauFall-tauRise -> 0 then we have a alphasynapse.
and if tauRise -> 0 then we have just single exponential decay.

The factor is evaluated in the
initial block such that an event of weight 1 generates a
peak conductance of 1.

Because the solution is a sum of exponentials, the
coupled equations can be solved as a pair of independent equations
by the more efficient cnexp method.

ENDCOMMENT

NEURON {
	POINT_PROCESS DCNsyn
	NONSPECIFIC_CURRENT i
	RANGE g, i, e, tauRise, tauFall
}

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

PARAMETER {
	tauRise = 1 (ms)
	tauFall = 1 (ms)
	e = 0 (mV)
}

ASSIGNED {
	v (mV)
	i (nA)
	g (microsiemens)
	factor
}

STATE {
	A (microsiemens)
	B (microsiemens)
}

INITIAL {
	LOCAL tp
	if (tauRise/tauFall > .9999) {
		tauRise = .9999*tauFall
	}
	A = 0
	B = 0
	tp = (tauRise*tauFall)/(tauFall - tauRise) * log(tauFall/tauRise)
	factor = -exp(-tp/tauRise) + exp(-tp/tauFall)
	factor = 1/factor
}

BREAKPOINT {
	SOLVE state METHOD cnexp
	g = B - A
	i = g*(v - e)
}

DERIVATIVE state {
	A' = -A/tauRise
	B' = -B/tauFall
}

NET_RECEIVE(weight (microsiemens)) {
	state_discontinuity(A, A + weight*factor)
	state_discontinuity(B, B + weight*factor)
}

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