Retinal ganglion cells responses and activity (Tsai et al 2012, Guo et al 2016)

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Accession:260653
From the abstracts: "Retinal ganglion cells (RGCs), which survive in large numbers following neurodegenerative diseases, could be stimulated with extracellular electric pulses to elicit artificial percepts. How do the RGCs respond to electrical stimulation at the sub-cellular level under different stimulus configurations, and how does this influence the whole-cell response? At the population level, why have experiments yielded conflicting evidence regarding the extent of passing axon activation? We addressed these questions through simulations of morphologically and biophysically detailed computational RGC models on high performance computing clusters. We conducted the analyses on both large-field RGCs and small-field midget RGCs. ...", "... In this study, an existing RGC ionic model was extended by including a hyperpolarization activated non-selective cationic current as well as a T-type calcium current identified in recent experimental findings. Biophysically-defined model parameters were simultaneously optimized against multiple experimental recordings from ON and OFF RGCs. ...
References:
1 . Guo T, Tsai D, Morley JW, Suaning GJ, Kameneva T, Lovell NH, Dokos S (2016) Electrical activity of ON and OFF retinal ganglion cells: a modelling study. J Neural Eng 13:025005 [PubMed]
2 . Tsai D, Chen S, Protti DA, Morley JW, Suaning GJ, Lovell NH (2012) Responses of retinal ganglion cells to extracellular electrical stimulation, from single cell to population: model-based analysis. PLoS One 7:e53357 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Synapse; Extracellular;
Brain Region(s)/Organism: Retina;
Cell Type(s): Retina ganglion GLU cell;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Oscillations; Activity Patterns; Development;
Implementer(s): Tsai, David [d.tsai at unsw.edu.au];
Search NeuronDB for information about:  Retina ganglion GLU cell;
/////////////////////////////////////////////////////////////////////
// section name and topology

{load_file("rgc-ctt3219m.asc")}

objref dendrites
dendrites = new SectionList()
dend1 dendrites.append()
dend2 dendrites.append()

forall {
    //ensure odd nseg count
    if (nseg % 2 == 0) {
        nseg += 1
    }
}
soma {
    diam = 11.6502  //hard spec
    nseg = 5
}
ah[0] { 
    diam = 3  //hard spec
    L = 40  //hard spec
    nseg = 15
}


/////////////////////////////////////////////////////////////////////
// additional geometry for ais narrowr axon

create ais, narrowr, axon
ais {
    diam = 0.8
    L = 40
    nseg = 15
}
narrowr {
    diam = 0.8
    L = 90
    nseg = 15
}
axon {
    diam = 1
    L = 5300
    nseg = int(L/7)
}

connect ais(0), ah[0](1)
connect narrowr(0), ais(1)
connect axon(0), narrowr(1)
define_shape()


/////////////////////////////////////////////////////////////////////
// biophysics

forall insert pas
forall insert spike
forall ena = 35.0
forall ek = -75
forall insert cad
forall g_pas = 0.000005
forall e_pas = -62.5
forall Ra=110

forsec dendrites {
    gnabar_spike = 0.025
    gkbar_spike  = 0.012
    gabar_spike  = 0.036
    gcabar_spike = 0.002
    gkcbar_spike = 0.000001
}

soma {
    gnabar_spike = 0.070
    gkbar_spike  = 0.018
    gabar_spike  = 0.054
    gcabar_spike = 0.0015
    gkcbar_spike = 0.000065
}

ah[0] {
    gnabar_spike = 0.070
    gkbar_spike  = 0.018
    gabar_spike  = 0.054
    gcabar_spike = 0.0015
    gkcbar_spike = 0.000065
}

ais {
    gnabar_spike = 2.100  //30x of soma
    gkbar_spike  = 0.054  //matched
    gabar_spike  = 0.054
    gcabar_spike = 0.0015
    gkcbar_spike = 0.000065
}

narrowr {
    gnabar_spike = 0.100
    gkbar_spike  = 0.018
    gabar_spike  = 0.054
    gcabar_spike = 0.0015
    gkcbar_spike = 0.000065
}

axon {
    gnabar_spike = 0.070
    gkbar_spike  = 0.018
    gabar_spike  = 0
    gcabar_spike = 0
    gkcbar_spike = 0.000065
}

access soma
forall depth_cad = diam / 2


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