Purkinje neuron network (Zang et al. 2020)

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Accession:266799
Both spike rate and timing can transmit information in the brain. Phase response curves (PRCs) quantify how a neuron transforms input to output by spike timing. PRCs exhibit strong firing-rate adaptation, but its mechanism and relevance for network output are poorly understood. Using our Purkinje cell (PC) model we demonstrate that the rate adaptation is caused by rate-dependent subthreshold membrane potentials efficiently regulating the activation of Na+ channels. Then we use a realistic PC network model to examine how rate-dependent responses synchronize spikes in the scenario of reciprocal inhibition-caused high-frequency oscillations. The changes in PRC cause oscillations and spike correlations only at high firing rates. The causal role of the PRC is confirmed using a simpler coupled oscillator network model. This mechanism enables transient oscillations between fast-spiking neurons that thereby form PC assemblies. Our work demonstrates that rate adaptation of PRCs can spatio-temporally organize the PC input to cerebellar nuclei.
Reference:
1 . Zang Y, Hong S, De Schutter E (2020) Firing rate-dependent phase responses of Purkinje cells support transient oscillations. Elife [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Realistic Network;
Brain Region(s)/Organism: Cerebellum;
Cell Type(s): Cerebellum Purkinje GABA cell;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; MATLAB;
Model Concept(s): Phase Response Curves; Action Potentials; Spatio-temporal Activity Patterns; Synchronization; Action Potential Initiation; Oscillations;
Implementer(s): Zang, Yunliang ; Hong, Sungho [shhong at oist.jp];
Search NeuronDB for information about:  Cerebellum Purkinje GABA cell;
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PRC_network_code
figure4
mod
abBK.mod *
apthreshold.mod *
CaP_Raman.mod *
cdp_spiny.mod *
cdp10AIS.mod *
cdp20N_FD2.mod *
cdp4N.mod *
distr.mod *
ihnew.mod *
Isinunoisy.mod *
kv11.mod *
Kv1A.mod *
kv3.mod *
Kv34.mod *
kv4hybrid2.mod *
kv4s.mod *
mslo.mod *
nap.mod *
peak.mod *
pkjlk.mod *
rsgold.mod *
SK2.mod *
syn2.mod *
TCa.mod *
job_script *
                            
: Ih current
: Created 8/6/02 - nwg
: for the formulation of Angelo's data, the original parameter correspond to a maximum value of 250 ms. In their supplement data, this value is about 325 ms. So I corrected a value1.3
NEURON {
	SUFFIX hpkj
	NONSPECIFIC_CURRENT i
	RANGE ghbar, eh, i
	GLOBAL ninf, ntau
:    THREADSAFE	
}

UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)
	(S) = (siemens)
}
CONSTANT {
	q10 = 3

}
PARAMETER {
	v	 	(mV)
	celsius (degC)
	ghbar = .0001	(S/cm2)

	eh = -30	(mV)
}

ASSIGNED {
	i (mA/cm2)
	qt
	ninf
	ntau
}

STATE {
	n
}

INITIAL {
    qt = q10^((celsius-22 (degC))/10 (degC))
	rates(v)
	n = ninf
}

BREAKPOINT {
	SOLVE states METHOD cnexp
	i = ghbar*n*(v - eh)
}

DERIVATIVE states {
	rates(v)
	n' = (ninf - n)/ntau
}

PROCEDURE rates(v (mV)) {
:	ninf = 1/(1+exp((v+90.3+3)/9.9))
	ninf = 1/(1+exp((v+90.3+3+3)/9.67))

	ntau = 1000/(0.62*(exp((v+68)/-22)+exp((v+68)/7.14)))/qt/1.3
}

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