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
                            
// Calculate impedance of dendritic branches. Code taken from Neuron forum from post by Ted Carnevale:
// http://www.neuron.yale.edu/phpbb/viewtopic.php?f=13&t=139

//load_file("nrngui.hoc")

// load Maarten's morphology file
//load_file("aCC-L3-neuron.hoc")

// specify reasonable values for nseg--see
//   http://www.neuron.yale.edu/neuron/static/docs/d_lambda/d_lambda.html
freq = 100      // Hz, frequency at which AC length constant will be computed
d_lambda = 0.1
func lambda_f() { // currently accessed section, $1 == frequency
  return 1e5*sqrt(diam/(4*PI*$1*Ra*cm))
}

proc geom_nseg() {
  soma area(0.5) // make sure diam reflects 3d points
  forall { nseg = int((L/(d_lambda*lambda_f(freq))+0.9)/2)*2 + 1  }
}

geom_nseg()

// prepare to use Impedance class

// always a good idea to finitialize before computing impedance
v_init=-65
finitialize(v_init)

// demonstrate use of impedance class
objref zz
zz = new Impedance()
FREQ = 100 // Hz

WHERE = 0.5 // location in the soma that is the reference point
soma distance(0, WHERE)  // sets origin for distance calculations

iterator mysections() { local i
  for i = 2, numarg() {
    $&1 = $i //soma[$i]
    iterator_statement
  }
}

proc calcZ() {
  soma zz.loc(WHERE)  // sets origin for impedance calculations
  
    zz.compute()// assumes conductances depend only on v, i.e.
    		// ignores the impedance contributions of gating state differential equations
  
  /*zz.compute(FREQ) // takes the impedance contributions of 
                      // gating state differential equations into account
                      // but requires mechanisms to be compatible with CVODE
*/
  // select some sections
  print "x \tdistance(x) \tinput(x) \tinput_phase(x) \ttransfer(x) \ttransfer_phase(x) \tratio(x)"
  for mysections(&x, 0, 1, 3, 551, 312) {
    access soma[x]
    print secname()
    for (x) print x, "\t", distance(x), "\t", zz.input(x), "\t", zz.input_phase(x), "\t", zz.transfer(x), "\t", zz.transfer_phase(x), "\t", zz.ratio(x)
  }
}

calcZ()