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]
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
03_GrC_2020_adapting
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
        KM channel
   
	Author: A. Fontana
	CoAuthor: T.Nieus Last revised: 20.11.99
	
ENDCOMMENT
 
NEURON { 
	SUFFIX GRC_KM 
	USEION k READ ek WRITE ik 
	RANGE gkbar, ik, g, alpha_n, beta_n 
	RANGE Aalpha_n, Kalpha_n, V0alpha_n
	RANGE Abeta_n, Kbeta_n, V0beta_n
	RANGE V0_ninf, B_ninf
	RANGE n_inf, tau_n 
} 
 
UNITS { 
	(mA) = (milliamp) 
	(mV) = (millivolt) 
} 
 
PARAMETER { 
	Aalpha_n = 0.0033 (/ms)
	Kalpha_n = 40 (mV)

	V0alpha_n = -30 (mV)
	Abeta_n = 0.0033 (/ms)
	Kbeta_n = -20 (mV)

	V0beta_n = -30 (mV)
	V0_ninf = -35 (mV)	:-30
	B_ninf = 6 (mV)		:6:4 rimesso a 6 dopo calibrazione febbraio 2003	
	v (mV) 
	gkbar= 0.00025 (mho/cm2) :0.0001
	ek = -84.69 (mV) 
	celsius = 30 (degC) 
} 

STATE { 
	n 
} 

ASSIGNED { 
	ik (mA/cm2) 
	n_inf 
	tau_n (ms) 
	g (mho/cm2) 
	alpha_n (/ms) 
	beta_n (/ms) 
} 
 
INITIAL { 
	rate(v) 
	n = n_inf 
} 
 
BREAKPOINT { 
	SOLVE states METHOD derivimplicit 
	g = gkbar*n 
	ik = g*(v - ek) 
	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 = 3^((celsius-22(degC))/10(degC)) 
	alp_n = Q10*Aalpha_n*exp((v-V0alpha_n)/Kalpha_n) 
} 
 
FUNCTION bet_n(v(mV))(/ms) { LOCAL Q10
	Q10 = 3^((celsius-22(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, V0_ninf, B_ninf, 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) 
	n_inf = 1/(1+exp(-(v-V0_ninf)/B_ninf))
} 

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