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;
/
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
                            
TITLE High voltage activated calcium current (CaHVA) of deep cerebellar nucleus (DCN) neuron
COMMENT
    Translated from GENESIS by Johannes Luthman and Volker Steuber. 
    This mechanism and the other calcium channel (CaLVA.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.

    Calcium entering the neuron through this channel is kept track of by CaConc.mod
    and affects the conductance of the SK.mod channel mechanism.
ENDCOMMENT 

NEURON { 
	SUFFIX CaHVA 
	USEION ca READ cai, cao WRITE ica
	RANGE perm, ica, m, cai
	GLOBAL qdeltat
} 
 
UNITS { 
	(mA) = (milliamp) 
	(mV) = (millivolt) 
	(molar) = (1/liter)
	(mM) = (millimolar)
} 
 
PARAMETER { 
    qdeltat = 1
    perm = 7.5e-6 (cm/seconds)
} 

ASSIGNED {
	v (mV)
    cai (mM)
    cao (mM)     
	ica (mA/cm2) 
	minf (1)
	taum (ms) 
	celsius (degC)
	T (kelvin)
    A (1)
} 

STATE {
	m
} 

INITIAL { 
    T = 273.15 + celsius
    rate(v)
    m = minf 
} 
 
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.
    ica = perm * m*m*m * (4.47814e6 * v / T) * ((cai/1000) - (cao/1000) * A) / (1 - A)
} 
 
DERIVATIVE states { 
	rate(v) 
	m' = (minf - m)/taum 
} 

PROCEDURE rate(v(mV)) {
	TABLE minf, taum FROM -150 TO 100 WITH 300
    minf = 1 / (1 + exp((v + 34.5) / -9))
    taum = 1 / ((31.746 * ((exp((v - 5) / -13.89) + 1) ^ -1)) + (3.97e-4 * (v + 8.9)) * ((exp((v + 8.9) / 5) - 1) ^ -1))
    taum = taum / 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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