Information transmission in cerebellar granule cell models (Rossert et al. 2014)

 Download zip file 
Help downloading and running models
Accession:156733
" ... In this modeling study we analyse how electrophysiological granule cell properties and spike sampling influence information coded by firing rate modulation, assuming no signal-related, i.e., uncorrelated inhibitory feedback (open-loop mode). A detailed one-compartment granule cell model was excited in simulation by either direct current or mossy-fiber synaptic inputs. Vestibular signals were represented as tonic inputs to the flocculus modulated at frequencies up to 20 Hz (approximate upper frequency limit of vestibular-ocular reflex, VOR). Model outputs were assessed using estimates of both the transfer function, and the fidelity of input-signal reconstruction measured as variance-accounted-for. The detailed granule cell model with realistic mossy-fiber synaptic inputs could transmit infoarmation faithfully and linearly in the frequency range of the vestibular-ocular reflex. ... "
Reference:
1 . Rössert C, Solinas S, D'Angelo E, Dean P, Porrill J (2014) Model cerebellar granule cells can faithfully transmit modulated firing rate signals. Front Cell Neurosci 8:304 [PubMed]
Citations  Citation Browser
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Synapse;
Brain Region(s)/Organism: Cerebellum;
Cell Type(s): Cerebellum interneuron granule GLU cell;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; Python;
Model Concept(s): Action Potentials; Markov-type model;
Implementer(s): Solinas, Sergio [solinas at unipv.it]; Roessert, Christian [christian.a at roessert.de];
Search NeuronDB for information about:  Cerebellum interneuron granule GLU cell;
TITLE Cerebellum Granule Cell Model

COMMENT
        KDr channel
	Gutfreund parametrization
   
	Author: A. Fontana
	Last revised: 12.12.98
ENDCOMMENT

NEURON { 
	SUFFIX GRANULE_KV 
	USEION k READ ek WRITE ik 
	RANGE Q10_diff,Q10_channel,gbar_Q10
	RANGE gbar, ic, g, alpha_n, beta_n 
	RANGE Aalpha_n, Kalpha_n, V0alpha_n
	RANGE Abeta_n, Kbeta_n, V0beta_n
	RANGE n_inf, tau_n 
} 
 
UNITS { 
	(mA) = (milliamp) 
	(mV) = (millivolt) 
} 
 
PARAMETER { 
	:Kbeta_n = -0.0125 (/mV)
	
	Aalpha_n = -0.01 (/ms-mV)
	Kalpha_n = -10 (mV)
	V0alpha_n = -25 (mV)
	Abeta_n = 0.125 (/ms)
	
	Kbeta_n = -80 (mV)
	V0beta_n = -35 (mV)
	v (mV)  
	gbar= 0.003 (mho/cm2)
	ek = -84.69 (mV) 
	Q10_diff	= 1.5
	Q10_channel	= 3
	celsius (degC)
} 

STATE { 
	n 
} 

ASSIGNED { 
	ik (mA/cm2) 
	ic (mA/cm2) 
	n_inf 
	tau_n (ms) 
	g (mho/cm2) 
	alpha_n (/ms) 
	beta_n (/ms) 
	gbar_Q10 (mho/cm2)
} 
 
INITIAL { 
	gbar_Q10 = gbar*(Q10_diff^((celsius-30)/10))
	rate(v) 
	n = n_inf 
} 
 
BREAKPOINT { 
	SOLVE states METHOD cnexp 
	g = gbar_Q10*n*n*n*n 
	ik = g*(v - ek) 
	ic = ik
	alpha_n = alp_n(v) 
	beta_n = bet_n(v) 
} 
 
DERIVATIVE states { 
	rate(v) 
	n' =(n_inf - n)/tau_n 
} 
 
FUNCTION alp_n(v(mV))(/ms) { LOCAL Q10
	Q10 = Q10_channel^((celsius-6.3(degC))/10(degC)) 
	alp_n = Q10*Aalpha_n*linoid(v-V0alpha_n, Kalpha_n)
} 
 
FUNCTION bet_n(v(mV))(/ms) { LOCAL Q10
	Q10 = Q10_channel^((celsius-6.3(degC))/10(degC)) 
	bet_n = Q10*Abeta_n*exp((v-V0beta_n)/Kbeta_n) 
} 
 
PROCEDURE rate(v (mV)) {LOCAL a_n, b_n 
	TABLE n_inf, tau_n 
	DEPEND Aalpha_n, Kalpha_n, V0alpha_n, 
               Abeta_n, Kbeta_n, V0beta_n, celsius FROM -100 TO 30 WITH 13000 
	a_n = alp_n(v)  
	b_n = bet_n(v) 
	tau_n = 1/(a_n + b_n) 
	n_inf = a_n/(a_n + b_n) 
} 

FUNCTION linoid(x (mV),y (mV)) (mV) {
        if (fabs(x/y) < 1e-6) {
                linoid = y*(1 - x/y/2)
        }else{
                linoid = x/(exp(x/y) - 1)
        }
}