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
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 Cerebellum Granule Cell Model leakage

COMMENT
	Reference: Theta-Frequency Bursting and Resonance in Cerebellar Granule Cells:Experimental
	Evidence and Modeling of a Slow K+-Dependent Mechanism
	Egidio D'Angelo,Thierry Nieus,Arianna Maffei,Simona Armano,Paola Rossi,Vanni Taglietti,
	Andrea Fontana and Giovanni Naldi
	
	SAT: its a distributed mechanism with out any dynamic behaviour. Its just modified from Granular cell's leakage channel.
ENDCOMMENT
 
NEURON { 
	SUFFIX Ubc_TRP 
	NONSPECIFIC_CURRENT itrp
	:USEION cAMP READ cAMPi VALENCE 0
	USEION ca READ cai
	USEION con_2c READ con_2ci VALENCE 1
	RANGE etrp, gtrp, i, itrp, fcAMP, TonicTRP
} 
 
UNITS { 
	(mA) = (milliamp) 
	(mV) = (millivolt) 
} 
 
PARAMETER { 
	v (mV) 
	gtrp = 4.18e-6 (mho/cm2)
	celsius = 30 (degC)
	etrp = 0 (mV)
	fcAMP = 1
	theta = 0 (1)
	TonicTRP = 0.05 
    } 
    
    ASSIGNED { 
	cAMPi  (mM)
	cai (mM)
	itrp (mA/cm2) 
	i (mA/cm2)
	con_2ci (mM)
    }
    
    BREAKPOINT { 
	:printf("cai %g\n",cai)
	if(con_2ci > 0.0075){
	     itrp = (gtrp*(v - etrp) * (1 + (3*con_2ci)))
         }else {
          itrp = 0    :gtrp*(v - etrp) *0.01
	}
	i = itrp
	: printf("cAMPi: %g \t itrp: %g",cAMPi)
} 

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