Cerebellar granule cell (Masoli et al 2020)

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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;
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Granule_cell_2020
01_GrC_2020_regular
mod_files
cdp5_CR.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 *
                            
TITLE Cerebellum Granule Cell Model

COMMENT
Based on Raman 13 state model. Adapted from Magistretti et al, 2006.
ENDCOMMENT

NEURON {
	SUFFIX GRC_NA
	USEION na READ ena WRITE ina
	RANGE gnabar, ina, g
	RANGE alfa, beta, gamma, delta, epsilon, teta, Con, Coff, Oon, Ooff
	RANGE Aalfa, Valfa, Abeta, Vbeta, Ateta, Vteta, Agamma, Adelta, Aepsilon, ACon, ACoff, AOon, AOoff
	RANGE n1,n2,n3,n4, alpha_d, beta_d, teta_d
}

UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)
}

PARAMETER {
	v (mV)
	celsius = 32  	(degC)
	ena = 87.39		(mV)
	gnabar = 0.013	(mho/cm2)
	
	Aalfa = 353.91 ( /ms)
	Valfa = 13.99 ( /mV) 
	Abeta = 1.272  ( /ms)
	Vbeta = 13.99 ( /mV)
	Agamma = 150 ( /ms)
	Adelta = 40  ( /ms)
	Aepsilon = 1.75 ( /ms)
	Ateta = 0.0201 ( /ms)
	Vteta = 25
	
	ACon = 0.005    ( /ms) 
	ACoff = 0.5     ( /ms)
	AOon = 0.75     ( /ms)
	AOoff = 0.005   ( /ms)
	
	n1 = 5.422
	n2 = 3.279
	n3 = 1.83
	n4 = 0.738
}

ASSIGNED {
	ina  (mA/cm2)
	g   (mho/cm2)
	
	gamma
	delta
	epsilon
	Con
	Coff
	Oon
	Ooff
	a
	b
	Q10
	:alpha_d
	:beta_d
	:teta_d	
}

STATE {
	C1
	C2
	C3
	C4
	C5
	O
	OB
	I1
	I2
	I3
	I4
	I5
	I6
}


INITIAL {
	C1=1
	C2=0
	C3=0
	C4=0
	C5=0
	O=0
	OB=0
	I1=0
	I2=0
	I3=0
	I4=0
	I5=0
	I6=0
	Q10 =3^((celsius-20(degC))/10 (degC))
	gamma = Q10 * Agamma
	delta = Q10 * Adelta
	epsilon = Q10 * Aepsilon
	Con = Q10 * ACon
	Coff = Q10 * ACoff
	Oon = Q10 * AOon
	Ooff = Q10 * AOoff
	a = (Oon/Con)^0.25
	b = (Ooff/Coff)^0.25

}

BREAKPOINT {
	SOLVE kstates METHOD sparse
	g = gnabar * O	      	: (mho/cm2)
	ina = g * (v - ena)  	: (mA/cm2)
	:alpha_d = alfa(v) 
	:beta_d = beta(v) 
	:teta_d = teta(v) 
}


FUNCTION alfa(v(mV))(/ms){ 
	alfa = Q10*Aalfa*exp(v/Valfa) 
}

FUNCTION beta(v(mV))(/ms){ 
	beta = Q10*Abeta*exp(-v/Vbeta) 
}

FUNCTION teta(v(mV))(/ms){ 
	teta = Q10*Ateta*exp(-v/Vteta) 
}
 

KINETIC kstates {
	: 1 riga
	~ C1 <-> C2 (n1*alfa(v),n4*beta(v))
	~ C2 <-> C3 (n2*alfa(v),n3*beta(v))
	~ C3 <-> C4 (n3*alfa(v),n2*beta(v))
	~ C4 <-> C5 (n4*alfa(v),n1*beta(v))
	~ C5 <-> O  (gamma,delta)
	~  O <-> OB (epsilon,teta(v))
	
	: 2 riga
	~ I1 <-> I2	(n1*alfa(v)*a,n4*beta(v)*b)
	~ I2 <-> I3	(n2*alfa(v)*a,n3*beta(v)*b)
	~ I3 <-> I4	(n3*alfa(v)*a,n2*beta(v)*b)
	~ I4 <-> I5 (n4*alfa(v)*a,n1*beta(v)*b)
	~ I5 <-> I6 (gamma,delta)
	
	: connette 1 riga con 2 riga
	~ C1 <-> I1 (Con,Coff)
	~ C2 <-> I2 (Con*a,Coff*b)
	~ C3 <-> I3 (Con*a^2,Coff*b^2)
	~ C4 <-> I4 (Con*a^3,Coff*b^3)
	~ C5 <-> I5 (Con*a^4,Coff*b^4)
	~  O <-> I6 (Oon,Ooff)
	
	CONSERVE C1+C2+C3+C4+C5+O+OB+I1+I2+I3+I4+I5+I6=1
}