DG adult-born granule cell: nonlinear a5-GABAARs control AP firing (Lodge et al, 2021)

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Accession:267106
GABA can depolarize immature neurons close to the action potential (AP) threshold in development and adult neurogenesis. Nevertheless, GABAergic synapses effectively inhibit AP firing in newborn granule cells of the adult hippocampus as early as 2 weeks post mitosis. Parvalbumin and dendrite-targeting somatostatin interneurons activate a5-subunit containing GABAA receptors (a5-GABAARs) in young neurons, which show a voltage dependent conductance profile with increasing conductance around the AP threshold. The present computational models show that the depolarized GABA reversal potential promotes NMDA receptor activation. However, the voltage-dependent conductance of a5-GABAARs in young neurons is crucial for inhibition of AP firing to generate balanced and sparse firing activity.
Reference:
1 . Lodge M, Hernandez MC, Schulz JM, Bischofberger J (2021) Sparsification of AP firing in adult-born hippocampal granule cells via voltage-dependent a5-GABAA receptors Cell Reports [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: Dentate gyrus;
Cell Type(s): Dentate gyrus granule GLU cell;
Channel(s): I K; I Krp; I Na,t;
Gap Junctions:
Receptor(s): AMPA; GabaA; NMDA;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Action Potentials; Detailed Neuronal Models; Development; Neurogenesis; Pattern Separation; Synaptic Integration;
Implementer(s): Schulz, Jan M [j.schulz at unibas.ch]; Bischofberger, Josef;
Search NeuronDB for information about:  Dentate gyrus granule GLU cell; GabaA; AMPA; NMDA; I Na,t; I K; I Krp; Gaba; Glutamate;
// Number of glutamatergic and GABAergic synapses
// 
n_glu_syn=3*ndend*(2*2*2+1) 
//Attention: each anatomic GABAergic synapse is modeled by two netcons: 
// one linear connection + one voltage-dependent representing different receptors 
n_GABA_syn=3*2*ndend*(2*2*2+1)   
// synaptic inputs
objref nsGABA[n_GABA_syn],ncGABA[n_GABA_syn],synGABA[n_GABA_syn]
objref nsAMPA[n_glu_syn],ncAMPA[n_glu_syn],synAMPA[n_glu_syn]
objref nsNMDA[n_glu_syn],ncNMDA[n_glu_syn],synNMDA[n_glu_syn]
objref locations_glu, locations_gaba, dendrite_ID 

// insert Glu and GABA synapses
//x=0.5
locations_glu = new Vector()
locations_gaba = new Vector()
dendrite_ID= new SectionList()

forsec middleDen{
    locations_glu.append(0.45)
    locations_gaba.append(0.15)
    dendrite_ID.append()
    }
forsec distalDen{
    locations_glu.append(0.7)
    locations_gaba.append(0.4)
    dendrite_ID.append()
    }
forsec proxDen{
    locations_glu.append(0.85)
    locations_gaba.append(0.55)
    dendrite_ID.append()
    }
forsec middleDen{
    locations_glu.append(0.85)
    locations_gaba.append(0.55)
    dendrite_ID.append()
    }
forsec middleDen{
    locations_glu.append(0.45)
    locations_gaba.append(0.15)
    dendrite_ID.append()
    }
forsec distalDen{
    locations_glu.append(0.7)
    locations_gaba.append(0.4)
    dendrite_ID.append()
    }
forsec proxDen{
    locations_glu.append(0.85)
    locations_gaba.append(0.55)
    dendrite_ID.append()
    }
forsec middleDen{
    locations_glu.append(0.85)
    locations_gaba.append(0.55)
    dendrite_ID.append()
    }
forsec middleDen{
    locations_glu.append(0.45)
    locations_gaba.append(0.15)
    dendrite_ID.append()
    }
forsec distalDen{
    locations_glu.append(0.7)
    locations_gaba.append(0.4)
    dendrite_ID.append()
    }
forsec proxDen{
    locations_glu.append(0.85)
    locations_gaba.append(0.55)
    dendrite_ID.append()
    }
forsec middleDen{
    locations_glu.append(0.85)
    locations_gaba.append(0.55)
    dendrite_ID.append()
    }
//for (ii=0; ii<n_glu_syn; ii=ii+1){
ii=0
forsec dendrite_ID{
    //access dendrite_ID.x[ii]
    synAMPA[ii] = new excSyn(locations_glu.x[ii])
    //dend synAMPA[ii] = new excSyn(x)
    synAMPA[ii].isOn=0
    nsAMPA[ii] = new NetStim()
    ncAMPA[ii] = new NetCon(nsAMPA[ii], synAMPA[ii])

    synNMDA[ii] = new Exp2SynNmda(locations_glu.x[ii])
    //dend synNMDA[ii] = new Exp2SynNmda(x)
    synNMDA[ii].isOn=0
    nsNMDA[ii] = new NetStim()
    ncNMDA[ii] = new NetCon(nsNMDA[ii], synNMDA[ii])
    ii+=1
}

//x=0.5
//for (ii=1; ii<2*n_GABA_syn+1; ii=ii+1){
ii=1
forsec dendrite_ID{
    //access dendrite_ID.x[ii-1]
    synGABA[ii*2-2] = new inhSyn(locations_gaba.x[ii-1])
    synGABA[ii*2-2].isOn=0
    nsGABA[ii*2-2] = new NetStim()
    ncGABA[ii*2-2] = new NetCon(nsGABA[ii*2-2], synGABA[ii*2-2])
    synGABA[ii*2-1] = new inhSyn(locations_gaba.x[ii-1])
    synGABA[ii*2-1].isOn=0
    nsGABA[ii*2-1] = new NetStim()
    ncGABA[ii*2-1] = new NetCon(nsGABA[ii*2-1], synGABA[ii*2-1])
    ii+=1
}

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