Drosophila 3rd instar larval aCC motoneuron (Gunay et al. 2015)

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Accession:152028
Single compartmental, ball-and-stick models implemented in XPP and full morphological model in Neuron. Paper has been submitted and correlates anatomical properties with electrophysiological recordings from these hard-to-access neurons. For instance we make predictions about location of the spike initiation zone, channel distributions, and synaptic input parameters.
Reference:
1 . Günay C, Sieling FH, Dharmar L, Lin WH, Wolfram V, Marley R, Baines RA, Prinz AA (2015) Distal spike initiation zone location estimation by morphological simulation of ionic current filtering demonstrated in a novel model of an identified Drosophila motoneuron. PLoS Comput Biol 11:e1004189 [PubMed]
Citations  Citation Browser
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: Drosophila;
Cell Type(s):
Channel(s): I Na,p; I Na,t; I A; I K;
Gap Junctions:
Receptor(s): Cholinergic Receptors;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; XPP; MATLAB;
Model Concept(s):
Implementer(s): Gunay, Cengiz [cgunay at emory.edu]; Sieling, Fred [fred.sieling at gmail.com]; Prinz, Astrid [astrid.prinz at emory.edu];
Search NeuronDB for information about:  Cholinergic Receptors; I Na,p; I Na,t; I A; I K;
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Gunay_etal_2014
neuron-model
aCC-L3-neuron.hoc
aCC-L3-neuron+electrode.xml
aCC-L3-neuron-swc.hoc
calc-impedance.hoc
chan-DmKA-Marley.hoc
chan-DmKdr-Marley.hoc
chan-DmNaP-DmNav10.hoc
chan-DmNaT-ODowd.hoc
collapse-neuron-tree.hoc
current-inj-50pA-read-mV_dt_0.025ms.bin
data-axon-tail2-axon-50um-vc-noKdr-long-back-85mV-Na_4_lines_dt_0.025000ms.bin
data-axon-tail2-axon-70um-vc-noKdr-long-back-85mV-Na_4_lines_dt_0.025000ms.bin
data-axon-tail2-axon-70um-vc-noKdr-long-back-85mV-Na-5xNaP_4_lines_dt_0.025000ms.bin
data-axon-tail2-axon-70um-vc-noKdr-long-back-85mV-Na-5xNaT_4_lines_dt_0.025000ms.bin
data-axon-tail2-axon-70um-vc-noKdr-long-back-85mV-passive_4_lines_dt_0.025000ms.bin
data-axon-tail2-chans-axon_11_lines_dt_0.025000ms.bin
data-axon-tail2-chans-axon-last_11_lines_dt_0.025000ms.bin
data-axon-tail2-chans-botdend_11_lines_dt_0.025000ms.bin
data-axon-tail2-chans-ext-axon-70um_11_lines_dt_0.025000ms.bin
data-axon-tail2-chans-in-all_11_lines_dt_0.025000ms.bin
data-i-syn-10syns-20-EPSCs-10x-10ms-VC-60mV_6_lines_dt_0.025000ms.bin
data-i-syn-4dends-50-EPSCs-10x-10ms-VC-60mV_5_lines_dt_0.025000ms.bin
data-i-vclamp-syn-dend-513-180-EPSCs-10x-1ms-saturating_2_lines_dt_0.025000ms.bin
data-syn-dend-357_2_lines_dt_0.025000ms.bin
data-syn-dend-513_2_lines_dt_0.025000ms.bin
data-syn-dend-520_2_lines_dt_0.025000ms.bin
data-syn-dend-685_2_lines_dt_0.025000ms.bin
data-v-syn-10dends-20-EPSCs-10x-10ms-noVC_6_lines_dt_0.025000ms.bin
data-v-syn-4dends-50-EPSCs-10x-10ms-noVC_6_lines_dt_0.025000ms.bin
data-v-syn-dend-513-180-EPSCs-10x-1ms-saturating-noVC_5_lines_dt_0.025000ms.bin
data-v-syn-dend-685-AP_3_lines_dt_0.025000ms.bin
exp-axon-tail2.ses
exp-axon-tail2-chans-axon.ses
exp-axon-tail2-chans-axon-last.ses
exp-axon-tail2-chans-botdend.ses
exp-axon-tail2-chans-ext-axon-50um-onlyNa.ses
exp-axon-tail2-chans-ext-axon-70um.ses
exp-axon-tail2-chans-ext-axon-70um-10alphasynapses.ses
exp-axon-tail2-chans-ext-axon-70um-10x-mimic-sustained.ses
exp-axon-tail2-chans-ext-axon-70um-10x-mimic-sustained-random.ses
exp-axon-tail2-chans-ext-axon-70um-mimic-synapses.ses
exp-axon-tail2-chans-ext-axon-70um-mimic-synapses-sustained-currents.ses
exp-axon-tail2-chans-ext-axon-70um-mimic-synapses-v-change.ses
exp-axon-tail2-chans-ext-axon-70um-onlyNa.ses
exp-axon-tail2-chans-ext-axon-70um-tomasz.ses
exp-axon-tail2-chans-in-all.ses
figures.m
fitfuncs.hoc
graph-i-vc-ext-axon.ses
iclamp-50pA.ses
IClamp-steps.ses
inc-first.ses
lincir-vclamp.hoc
lincir-vclamp.ses
NaP_NaT_data.csv
neuron-CB.ses
neuron-CB+electrode.hoc
neuron-CB-act-electrode-embed-IClamp.ses
neuron-CB-ext-axon.ses
neuron-CB-ext-axon-2pieces.ses
neuron-CB-ext-axon-2pieces-chans-axon.ses
neuron-CB-ext-axon-2pieces-chans-axon-last.ses
neuron-CB-ext-axon-2pieces-chans-botdend.ses
neuron-CB-ext-axon-2pieces-chans-ext-axon-50um-onlyNa.ses
neuron-CB-ext-axon-2pieces-chans-ext-axon-70um.ses
neuron-CB-ext-axon-2pieces-chans-ext-axon-70um-10alphasynapses.ses *
neuron-CB-ext-axon-2pieces-chans-ext-axon-70um-10x-mimic-sustained.ses *
neuron-CB-ext-axon-2pieces-chans-ext-axon-70um-mimic-synapses.ses *
neuron-CB-ext-axon-2pieces-chans-ext-axon-70um-mimic-synapses-v-change.ses *
neuron-CB-ext-axon-2pieces-chans-ext-axon-70um-onlyNa.ses
neuron-CB-ext-axon-2pieces-chans-in-all.ses
neuron-CB-pas-electrode-embed.ses
neuron-CB-pas-electrode-embed-fit-pas.ses
neuron-CB-pas-electrode-embed-fit-pas-VClamp.ses
neuron-CB-pas-electrode-embed-IClamp.ses
neuron-CB-pas-electrode-embed-test-axon-hh-chans.ses
neuron-Import3D-CellBuilder.ses
neuron-NL-CellBuilder.ses
neuron-NL-CellBuilder-pas.ses
neuron-NL-CellBuilder-pas-electrode.ses
neuron-NL-CellBuilder-pas-Na.ses
neuron-PointProcessMgr-ext-axon-2pieces-chans-ext-axon-70um-10alphasynapses.ses
nrn-fit-cap-02_dt_0.025000ms_dy_1e-9nA.bin
shape-plot.ses
SkeletonTree_ORR_aCC_48h1_NL.hoc
soma-vclamp-testbed.ses
stats.hoc
vclamp_-85_to_-25mV.ses
vclamp_soma_-60mV.ses
vclamp_soma_-60mV_syn1234.ses
vclamp_soma_-60mV_syni.ses
vclamp-family.ses
v-graph.ses
v-graph-bigger.ses
v-graph-bigger-axon-2pieces.ses
                            
{load_file("nrngui.hoc")}
objectvar save_window_, rvp_
objectvar scene_vector_[2]
objectvar ocbox_, ocbox_list_, scene_, scene_list_
{ocbox_list_ = new List()  scene_list_ = new List()}
{pwman_place(0,0,0)}

//Begin SingleCompartment
{
load_file("single.hoc")
}
ocbox_ = new SingleCompartment(0)
ocbox_.inserter = new Inserter(0)
{object_push(ocbox_.inserter)}
{
mt.select("pas") i = mt.selected()
ms[i] = new MechanismStandard("pas")
ms[i].set("g_pas", 3.294e-05, 0)
ms[i].set("e_pas", -60, 0)
mstate[i]= 1
maction(i)
}
{object_pop() doNotify()}
{object_push(ocbox_)}
{inserter.v1.map()}
{endbox()}
{object_pop() doNotify()}
{
ocbox_ = ocbox_.vbox
ocbox_.map("SingleCompartment", 358, 137, 91.2, 96)
}
objref ocbox_
//End SingleCompartment

// calculate the real capacitance and g_pas values
access soma

cm = 1 
diam = 200 // Cm = 20 pF

// gLm=0.85 nS
g_pas = 0.85e-9/(PI*diam*L*1e-8)
e_pas = -85

//Begin LinearCircuit[0]
{
load_file("lincir.hoc", "LinearCircuit")
}
{
ocbox_ = new LinearCircuit(1)
}
{object_push(ocbox_)}
{version(2)}
{mkelm(1, 210, 140, 4, 0)}
38
{mklabel(0, "Re", 4.625, 30.374)}
{mklabel(1, "Vc", 16.162, 25.631)}
{restore_ic(1, -60)}
{mklabel(2, "Vm", 15, 15)}
{mkelm(6, 170, 70, 2, 0)}
{mkelm(1, 170, 115, 2.5, -1.5708)}
2000
{mklabel(0, "Rs", 20.906, 2.01)}
{mkelm(8, 250, 90, 2, 0)}
{mklabel(0, "soma(0.5)", 8.0724, -12.2756)}
{sel.extra_info.set("soma", 0.5) sel.extra_info.name(sel)}
{mkelm(4, 15, 160, 1.5, 3.14159)}
{mklabel(0, "VC", -4.3585, 24.605)}
{sel.extra_info.restore()}
3
10 -60
50 -90
10 -60
{mkelm(6, 0, 140, 2, 0)}
{mkelm(0, 250, 125, 1.5, -1.5708)}
{mkelm(7, 130, 140, 2, 0)}
100000
0
{mklabel(0, "Control", 0.423, 4.285)}
{mkelm(1, 130, 200, 4, 0)}
100000
{mklabel(0, "R2", 1.609, 22.553)}
{mkelm(1, 60, 160, 3, 0)}
100000
{mklabel(0, "R1", 1.6374, 22.233)}
{mkelm(6, 90, 100, 2, 0)}
{mkelm(0, 90, 180, 2, 1.5708)}
{mkelm(0, 170, 170, 3, -1.5708)}
{parasitic_ = 0  noconsist_ = 0}
{graphlist.append(new LincirGraph(this, 1))}
1
Control I (nA)
1 1 0.8 0.9 2
18.2747 23.0513 -0.720308 0.0326084 // graph size
25 137 308.16 261.12 // box size
// end info
{graphlist.append(new LincirGraph(this, 1))}
2
Vc (mV)
1 1 0.8 0.9 2
Vm (mV)
1 1 0.8 0.9 2
0 40 -90 0 // graph size
15 879 308.16 261.12 // box size
// end info
{g.exec_menu("Simulate")  tool(2)}
{sel = nil}
{object_pop()}
{
{
save_window_=ocbox_.g
save_window_.size(0,300,0,200)
scene_vector_[1] = save_window_
ocbox_.g = save_window_
save_window_.save_name("ocbox_.g")
save_window_.label(214.625, 170.374, "Re", 1, 1, 0.5, 0.5, 1)
save_window_.label(186.162, 165.631, "Vc", 1, 1, 0.5, 0.5, 1)
save_window_.label(265, 155, "Vm", 1, 1, 0.5, 0.5, 1)
save_window_.label(190.906, 117.01, "Rs", 1, 1, 0.5, 0.5, 1)
save_window_.label(258.072, 77.7244, "soma[1](0.5)", 1, 1, 0.5, 0.5, 1)
save_window_.label(10.6415, 184.605, "VC", 1, 1, 0.5, 0.5, 1)
save_window_.label(130.423, 144.285, "Control", 1, 1, 0.5, 0.5, 1)
save_window_.label(131.609, 222.553, "R2", 1, 1, 0.5, 0.5, 1)
save_window_.label(61.6374, 182.233, "R1", 1, 1, 0.5, 0.5, 1)
}
ocbox_.map("LinearCircuit[0]", 18, 374, 927.36, 496.32)
}
objref ocbox_
//End LinearCircuit[0]

// set lincir values

// Re
ElementBase[1].val.x[0] = 38

// Rs
ElementBase[3].val.x[0] = 1090

// R1, R2, gain
ElementBase[8].val.x[0] = 1e5
ElementBase[9].val.x[0] = 1e5
ElementBase[10].val.x[0] = 1e5

v_init = -60

{
xpanel("RunControl", 0)
v_init = -60
xvalue("Init","v_init", 1,"stdinit()", 1, 1 )
xbutton("Init & Run","run()")
xbutton("Stop","stoprun=1")
runStopAt = 5
xvalue("Continue til","runStopAt", 1,"{continuerun(runStopAt) stoprun=1}", 1, 1 )
runStopIn = 1
xvalue("Continue for","runStopIn", 1,"{continuerun(t + runStopIn) stoprun=1}", 1, 1 )
xbutton("Single Step","steprun()")
t = 0
xvalue("t","t", 2 )
tstop = 50
xvalue("Tstop","tstop", 1,"tstop_changed()", 0, 1 )
dt = 0.025
xvalue("dt","dt", 1,"setdt()", 0, 1 )
steps_per_ms = 40
xvalue("Points plotted/ms","steps_per_ms", 1,"setdt()", 0, 1 )
screen_update_invl = 0.05
xvalue("Scrn update invl","screen_update_invl", 1,"", 0, 1 )
realtime = 0
xvalue("Real Time","realtime", 0,"", 0, 1 )
xpanel(894,778)
}
objectvar scene_vector_[1]
{doNotify()}

// int & run, and then pick vector Control I and save to file