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; 
from neuron import h

class Synapse_py3:
    def __init__(self,source,target,section,weight = 1):
		
        self.input = h.NetStim(0.5)
        self.input.start = -10
        self.input.number = 1
        self.input.interval = 1e9
        self.weight = weight


        self.postsyns = {}

        if (type(source) == type('s')):
            sourcetype = source
           
                             
#granule cell     
        if sourcetype == 'mossy':
            if target.whatami == 'GrC_2020_mild':
                self.whatami = "syn_mossytoGrC_det"
                self.postsyns['AMPA'] = [h.GRANULE_Ampa_det_vi(0.9, sec=section)]
                self.postsyns['AMPA'][0].tau_facil=5
                self.postsyns['AMPA'][0].tau_rec=8 #25 
                self.postsyns['AMPA'][0].tau_1=1
                self.postsyns['AMPA'][0].gmax = 1200
                self.postsyns['AMPA'][0].U=0.43

                self.nc_syn = [h.NetCon(self.input,receptor[0],0,0.1,1) for receptor in self.postsyns.values()]
            
        elif sourcetype == 'mossynmda':
            if target.whatami == 'GrC_2020_mild':
                self.whatami = "syn_mossytoGrC_det_nmda"
                self.postsyns['NMDA'] = [h.GRANULE_Nmda_det_vi(0.9, sec=section)]
                self.postsyns['NMDA'][0].tau_facil=5
                self.postsyns['NMDA'][0].tau_rec=8 
                self.postsyns['NMDA'][0].tau_1=1
                self.postsyns['NMDA'][0].gmax = 18800  
                self.postsyns['NMDA'][0].U=0.43
                self.nc_syn = [h.NetCon(self.input,receptor[0],0,0.1,1) for receptor in self.postsyns.values()]
		
        else:
            print('SOURCE TYPE DOES NOT EXIST SOMETHING WRONG!!!!!!!!!')

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