Cerebellar nuclear neuron (Sudhakar et al., 2015)

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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;
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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
                            
TITLE Low voltage activated calcium current (CaLVA) of deep cerebellar nucleus (DCN) neuron
COMMENT
    This mechanism and the other calcium channel (CaHVA.mod) are the only channel
    mechanisms of the DCN model that use the GHK mechanism to calculate reversal
    potential. Thus, extracellular Ca concentration is of importance and shall be
    set from hoc to 2mM, using: "calo0_ca_ion = 2".

    The calcium that this channel lets through feeds into the CalConc.mod mechanism
    while calcium entry via the CaHVA channel is tracked by CalConc.mod.
ENDCOMMENT 

NEURON { 
	SUFFIX CaLVA 
	USEION cal READ cali, calo WRITE ical VALENCE 2
	RANGE perm, ical, m, h, cali
	GLOBAL qdeltat
} 
 
UNITS { 
	(mA) = (milliamp) 
	(mV) = (millivolt)
	(molar) = (1/liter)
	(mM) = (millimolar)
} 
 
PARAMETER { 
    qdeltat = 1
    perm = 1 (cm/seconds)
} 

ASSIGNED {
    v (mV)
    cali (mM)
    calo (mM)     
	ical (mA/cm2) 
	minf
	hinf
	taum (ms) 
	tauh (ms) 
	celsius (degC)
	T (kelvin)
    A (1)
} 
 
STATE {
	m
    h
} 

INITIAL { 
    T = 273.15 + celsius
    rate(v)
    m = minf 
	h = hinf
} 
 
BREAKPOINT { 
    SOLVE states METHOD cnexp 
    A = getGHKexp(v)
    : "4.47814e6 * v / T" in the following is the simplification of the GHK
    : current equation's (z^2 * F^2 * (0.001) * v) / (R * T). [*(0.001) is to get 
    : volt from NEURON's mV.] Together with the simplification in getGHKexp() 
    : (below), this speeds up the whole DCN simulation (without synapses) by 8%.
    : The division of the calcium concentrations (mM) by 1000 gives molar as 
    : required by the GHK current equation.
    ical = perm * m*m * h * (4.47814e6 * v / T) * ((cali/1000) - (calo/1000) * A) / (1 - A)
} 
 
DERIVATIVE states { 
	rate(v) 
	m' = (minf - m)/taum 
	h' = (hinf - h)/tauh 
} 

PROCEDURE rate(v(mV)) {
	TABLE minf, taum, hinf, tauh  FROM -150 TO 100 WITH 300 
	minf = 1 / (1 + exp((v + 56) / -6.2))
	taum = 0.333 / (exp((v + 131) / -16.7) + exp((v + 15.8) / 18.2)) + 0.204
    taum = taum / qdeltat
	hinf = 1 / (1 + exp((v + 80) / 4))
    if (v < -81) {
        tauh = 0.333 * exp((v + 466) / 66)
    } else {
        tauh = 0.333 * exp((v + 21) / -10.5) + 9.32
    }
    tauh = tauh / qdeltat
}

FUNCTION getGHKexp(v(mV)) {
    TABLE DEPEND T FROM -150 TO 100 WITH 300 
    getGHKexp = exp(-23.20764929 * v / T): =the calculated values of
            : getGHKexp = exp((-z * F * (0.001) * v) / (R * T)).
}

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