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
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 SK2 multi-state model Cerebellum Golgi Cell Model

COMMENT

Author:Sergio Solinas, Lia Forti, Egidio DAngelo
Based on data from: Hirschberg, Maylie, Adelman, Marrion J Gen Physiol 1998
Last revised: May 2007

Published in:
             Sergio M. Solinas, Lia Forti, Elisabetta Cesana, 
             Jonathan Mapelli, Erik De Schutter and Egidio D`Angelo (2008)
             Computational reconstruction of pacemaking and intrinsic 
             electroresponsiveness in cerebellar golgi cells
             Frontiers in Cellular Neuroscience 2:2

Suffix from SK2 to Kca2_2

ENDCOMMENT

NEURON{
	SUFFIX Kca2_2
	USEION ca READ cai
	USEION k READ ek WRITE ik 
	RANGE gkbar, g, ik, tcorr
}

UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)
	(molar) = (1/liter)
	(mM) = (millimolar)
}

PARAMETER {
	celsius  (degC)
	cai (mM)
	gkbar = 0.038 (mho/cm2)
	Q10 = 3 (1)
	diff = 3 (1) : diffusion factor

: rates ca-indipendent
	invc1 = 80e-3  ( /ms)
	invc2 = 80e-3  ( /ms)
	invc3 = 200e-3 ( /ms)

	invo1 = 1      ( /ms)
	invo2 = 100e-3 ( /ms)
	diro1 = 160e-3 ( /ms)
	diro2 = 1.2    ( /ms)

: rates ca-dipendent
	dirc2 = 200 ( /ms-mM )
	dirc3 = 160 ( /ms-mM )
	dirc4 = 80  ( /ms-mM )

}

ASSIGNED{ 
	v	(mV) 
	ek	(mV) 
	g	(mho/cm2) 
	ik	(mA/cm2) 
	invc1_t  ( /ms)
	invc2_t  ( /ms)
	invc3_t  ( /ms)
	invo1_t  ( /ms)
	invo2_t  ( /ms)
	diro1_t  ( /ms)
	diro2_t  ( /ms)
	dirc2_t  ( /ms-mM)
	dirc3_t  ( /ms-mM)
	dirc4_t  ( /ms-mM)
	tcorr	 (1)

	dirc2_t_ca  ( /ms)
	dirc3_t_ca  ( /ms)
	dirc4_t_ca  ( /ms)
} 

STATE {
	c1
	c2
	c3
	c4
	o1
	o2
}

BREAKPOINT{ 
	SOLVE kin METHOD sparse 
	g = gkbar*(o1+o2)	:(mho/cm2)
	ik = g*(v-ek)		:(mA/cm2)
} 

INITIAL{
	rate(celsius)
	SOLVE kin STEADYSTATE sparse
} 

KINETIC kin{ 
	rates(cai/diff) 
	~c1<->c2 (dirc2_t_ca, invc1_t) 
	~c2<->c3 (dirc3_t_ca, invc2_t) 
	~c3<->c4 (dirc4_t_ca, invc3_t) 
	~c3<->o1 (diro1_t, invo1_t) 
	~c4<->o2 (diro2_t, invo2_t) 
	CONSERVE c1+c2+c3+c4+o2+o1=1 
} 

FUNCTION temper (Q10, celsius (degC)) {
	temper = Q10^((celsius -23(degC)) / 10(degC)) 
}

PROCEDURE rates(cai(mM)){
	dirc2_t_ca = dirc2_t*cai
	dirc3_t_ca = dirc3_t*cai
	dirc4_t_ca = dirc4_t*cai 
} 

PROCEDURE rate (celsius(degC)) {
	tcorr = temper (Q10,celsius)
	invc1_t = invc1*tcorr  
	invc2_t = invc2*tcorr
	invc3_t = invc3*tcorr 
	invo1_t = invo1*tcorr 
	invo2_t = invo2*tcorr 
	diro1_t = diro1*tcorr 
	diro2_t = diro2*tcorr 
	dirc2_t = dirc2*tcorr
	dirc3_t = dirc3*tcorr
	dirc4_t = dirc4*tcorr
}

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