Cerebellar nuclear neuron (Sudhakar et al., 2015)

 Download zip file   Auto-launch 
Help downloading and running models
Accession:185513
"... In this modeling study, we investigate different forms of Purkinje neuron simple spike pause synchrony and its influence on candidate coding strategies in the cerebellar nuclei. That is, we investigate how different alignments of synchronous pauses in synthetic Purkinje neuron spike trains affect either time-locking or rate-changes in the downstream nuclei. We find that Purkinje neuron synchrony is mainly represented by changes in the firing rate of cerebellar nuclei neurons. ..."
Reference:
1 . Sudhakar SK, Torben-Nielsen B, De Schutter E (2015) Cerebellar Nuclear Neurons Use Time and Rate Coding to Transmit Purkinje Neuron Pauses. PLoS Comput Biol 11:e1004641 [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: Cerebellum;
Cell Type(s): Cerebellum deep nucleus neuron;
Channel(s): I Na,p; I T low threshold; I h; I Sodium;
Gap Junctions:
Receptor(s): NMDA; Glutamate; Gaba;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Rate-coding model neurons; Rebound firing;
Implementer(s):
Search NeuronDB for information about:  NMDA; Glutamate; Gaba; I Na,p; I T low threshold; I h; I Sodium; Gaba; Glutamate;
/
SudhakarEtAl2015
readme.html
CaConc.mod *
CaHVA.mod *
CaL.mod
CalConc.mod *
CaLVA.mod *
DCNsyn.mod *
DCNsynGABA.mod
DCNsynNMDA.mod *
fKdr.mod *
GammaStim.mod *
h.mod *
Ifluct8.mod *
NaF.mod *
NaP.mod *
pasDCN.mod *
SK.mod *
sKdr.mod *
TNC.mod
vecevent.mod *
cellids.dat
cellids_n.dat
datasp_ex1.dat
datasp1.dat
DCN_init_model1.hoc
DCN_init_model2.hoc
DCN_init_model2_highgain.hoc
DCN_init_model2_lowgain.hoc
DCN_init_model2_medgain.hoc
DCN_init_model3.hoc
DCN_mechs1.hoc *
DCN_mechs2.hoc
DCN_morph.hoc *
DCN_params.hoc
l_ex1.dat
l1.dat
model1_params.hoc
model2_params.hoc
model2_params_highgain.hoc
model2_params_lowgain.hoc
model2_params_medgain.hoc
model3_params.hoc
mosinit.hoc
pausebeg.dat
pausebeg_n.dat
screenshot.png
                            
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)
}

Loading data, please wait...