Cerebellar granule cell (Masoli et al 2020)

 Download zip file   Auto-launch 
Help downloading and running models
Accession:265584
"The cerebellar granule cells (GrCs) are classically described as a homogeneous neuronal population discharging regularly without adaptation. We show that GrCs in fact generate diverse response patterns to current injection and synaptic activation, ranging from adaptation to acceleration of firing. Adaptation was predicted by parameter optimization in detailed computational models based on available knowledge on GrC ionic channels. The models also predicted that acceleration required additional mechanisms. We found that yet unrecognized TRPM4 currents specifically accounted for firing acceleration and that adapting GrCs outperformed accelerating GrCs in transmitting high-frequency mossy fiber (MF) bursts over a background discharge. This implied that GrC subtypes identified by their electroresponsiveness corresponded to specific neurotransmitter release probability values. Simulations showed that fine-tuning of pre- and post-synaptic parameters generated effective MF-GrC transmission channels, which could enrich the processing of input spike patterns and enhance spatio-temporal recoding at the cerebellar input stage."
Reference:
1 . Masoli S, Tognolina M, Laforenza U, Moccia F, D'Angelo E (2020) Parameter tuning differentiates granule cell subtypes enriching transmission properties at the cerebellum input stage. Commun Biol 3:222 [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;
Brain Region(s)/Organism: Cerebellum;
Cell Type(s): Cerebellum interneuron granule GLU cell;
Channel(s): Ca pump; I Na, leak; I Calcium;
Gap Junctions:
Receptor(s): AMPA; NMDA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; Python;
Model Concept(s): Action Potentials; Calcium dynamics; Synaptic Integration;
Implementer(s): Masoli, Stefano [stefano.masoli at unipv.it];
Search NeuronDB for information about:  Cerebellum interneuron granule GLU cell; AMPA; NMDA; I Calcium; I Na, leak; Ca pump;
/
Granule_cell_2020
04_GrC_2020_accelerating
mod_files
cdp5_CR_CAM.mod
GRANULE_Ampa_det_vi.mod *
GRANULE_Nmda_det_vi.mod *
GRC_CA.mod *
GRC_KM.mod *
GRC_NA.mod *
GRC_NA_FHF.mod *
Kca11.mod *
Kca22.mod *
Kir23.mod *
Kv11.mod *
Kv15.mod *
Kv22.mod *
Kv34.mod *
Kv43.mod *
Leak.mod *
UBC_TRP.mod
                            
TITLE Cardiac IKur  current & nonspec cation current with identical kinetics
: Hodgkin - Huxley type channels, modified to fit IKur data from Feng et al Am J Physiol 1998 275:H1717 - H 1725
: Suffix from Kv15 to Kv1_5

NEURON {
	SUFFIX Kv1_5
	USEION k READ ek,ki,ko WRITE ik
	USEION na READ nai,nao
	USEION no WRITE ino VALENCE 1: nonspecific cation current
	RANGE gKur, ik, ino, Tauact, Tauinactf,Tauinacts, gnonspec, nao, nai, ko,ki
	RANGE minf, ninf, uinf, mtau , ntau, utau
}

UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)
        (mM) = (milli/liter)
	F = (faraday) (coulombs)
	R 	= (k-mole)	(joule/degC)
}

PARAMETER {
	 gKur=0.13195e-3 (S/cm2) <0,1e9>
	Tauact=1 (ms)
	Tauinactf=1 (ms)
	Tauinacts=1 (ms)
	gnonspec=0   (S/cm2) <0,1e9>
}
STATE {
	 m n u
}

ASSIGNED {
	v (mV)
	celsius (degC) : 37
       	ik (mA/cm2)
	minf ninf uinf
	mtau (ms)
        ntau (ms)
	utau (ms)
	ek (mV)
	ino (mA/cm2)
	ki (mM)
	ko (mM)
	nai (mM)
	nao (mM)
}

INITIAL {
	rates(v)
	m = minf
        n = ninf
	u = uinf
}

BREAKPOINT { LOCAL z
	z = (R*(celsius+273.15))/F
	SOLVE states METHOD derivimplicit
		ik = gKur*(0.1 + 1/(1 + exp(-(v - 15)/13)))*m*m*m*n*u*(v - ek)
	ino=gnonspec*(0.1 + 1/(1 + exp(-(v - 15)/13)))*m*m*m*n*u*(v - z*log((nao+ko)/(nai+ki)))
}

DERIVATIVE states {	: exact when v held constant
	rates(v)
	m' = (minf - m)/mtau
        n' = (ninf - n)/ntau
	u' = (uinf - u)/utau
}

UNITSOFF
FUNCTION alp(v(mV),i) { LOCAL q10 : order m n
	v = v
	q10 = 2.2^((celsius - 37)/10)
       if (i==0) {
	          alp = q10*0.65/(exp(-(v + 10)/8.5) + exp(-(v - 30)/59))
          } else if (i==1) {
                   alp = 0.001*q10/(2.4 +10.9* exp(-(v + 90)/78))
          }
	
}

FUNCTION bet(v(mV),i) (/ms) { LOCAL q10 : order m n u
	v = v 
	q10 = 2.2^((celsius - 37)/10)
        if (i==0){
	         bet = q10*0.65/(2.5 + exp((v + 82)/17))
        }else if (i==1){
                  bet = q10*0.001*exp((v - 168)/16)
        }
}
                
FUNCTION ce(v(mV),i)(/ms) {   :  order m n u 
        v = v
       
        if (i==0) {
                ce = 1/(1 + exp(-(v + 30.3)/9.6))
        }else if (i==1){
                ce = 1*(0.25+1/(1.35 + exp((v + 7)/14)))
       
	}else if (i==2){
                ce = 1*(0.1+1/(1.1 + exp((v + 7)/14)))
        }
}


PROCEDURE rates(v) {LOCAL a,b,c :
	
		a = alp(v,0)  b=bet(v,0) c = ce(v,0)
		mtau = 1/(a + b)/3*Tauact
		minf = c
               a = alp(v,1)  b=bet(v,1) c = ce(v,1)
		ntau = 1/(a + b)/3*Tauinactf
		ninf = c
		c = ce(v,2)
		uinf = c
		utau = 6800*Tauinacts
}
UNITSON