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Motor cortex microcircuit simulation based on brain activity mapping (Chadderdon et al. 2014)

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Accession:146949
"... We developed a computational model based primarily on a unified set of brain activity mapping studies of mouse M1. The simulation consisted of 775 spiking neurons of 10 cell types with detailed population-to-population connectivity. Static analysis of connectivity with graph-theoretic tools revealed that the corticostriatal population showed strong centrality, suggesting that would provide a network hub. ... By demonstrating the effectiveness of combined static and dynamic analysis, our results show how static brain maps can be related to the results of brain activity mapping."
Reference:
1 . Chadderdon GL, Mohan A, Suter BA, Neymotin SA, Kerr CC, Francis JT, Shepherd GM, Lytton WW (2014) Motor cortex microcircuit simulation based on brain activity mapping. Neural Comput 26:1239-62 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex L5/6 pyramidal GLU cell; Neocortex M1 L2/6 pyramidal intratelencephalic GLU cell; Neocortex fast spiking (FS) interneuron; Neocortex spiking regular (RS) neuron; Neocortex spiking low threshold (LTS) neuron;
Channel(s):
Gap Junctions:
Receptor(s): GabaA; AMPA; NMDA;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Oscillations; Laminar Connectivity;
Implementer(s): Lytton, William [bill.lytton at downstate.edu]; Neymotin, Sam [Samuel.Neymotin at nki.rfmh.org]; Shepherd, Gordon MG [g-shepherd at northwestern.edu]; Chadderdon, George [gchadder3 at gmail.com]; Kerr, Cliff [cliffk at neurosim.downstate.edu];
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; Neocortex M1 L2/6 pyramidal intratelencephalic GLU cell; GabaA; AMPA; NMDA; Gaba; Glutamate;
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infot.mod *
intf6.mod *
intfsw.mod *
matrix.mod
misc.mod *
nstim.mod *
staley.mod *
stats.mod *
vecst.mod *
boxes.hoc *
col.hoc
declist.hoc *
decmat.hoc *
decnqs.hoc *
decvec.hoc *
default.hoc *
drline.hoc *
filtutils.hoc *
gcelldata.hoc
gmgs102.nqs
grvec.hoc *
infot.hoc *
init.hoc
intfsw.hoc *
labels.hoc *
load.py
local.hoc *
main.hoc
misc.h *
miscfuncs.py
network.hoc
neuroplot.py *
nload.hoc
nqs.hoc *
nqsnet.hoc
nrnoc.hoc *
params.hoc
run.hoc
samutils.hoc *
saveoutput.hoc
saveweights.hoc
setup.hoc *
simctrl.hoc *
spkts.hoc *
staley.hoc *
stats.hoc *
stdgui.hoc *
syncode.hoc *
updown.hoc *
wdmaps2.nqs
xgetargs.hoc *
                            
: $Id: nstim.mod,v 1.24 2006/04/03 19:18:18 billl Exp $

NEURON	{ 
  ARTIFICIAL_CELL NStim
  RANGE interval, number, start, end
  RANGE noise,type,id
}

PARAMETER {
  interval	= 10 (ms) <1e-9,1e9>: time between spikes (msec)
  number	= 10 <0,1e9>	: number of spikes
  start		= 50 (ms)	: start of first spike
  noise		= 0 <0,1>	: amount of randomeaness (0.0 - 1.0)
  end		= 1e9 (ms)	: time to terminate train
}

ASSIGNED {
  event (ms)
  on
  endt (ms)
  type
  id
}

CONSTRUCTOR {
  VERBATIM 
  { if (ifarg(1)) { id= *getarg(1); } else { id= -1; }
    if (ifarg(2)) { type= *getarg(2); } else { type= 1; }
  }
  ENDVERBATIM
}

PROCEDURE seed (x) {
  set_seed(x)
}

INITIAL {
  on = 0
  if (noise < 0) { noise = 0 }
  if (noise > 1) { noise = 1 }
  if (interval <= 0.) { interval = .01 (ms) }
  if (start>=0 && number>0 && end>0) {
    event = start + invl(interval) - interval*(1. - noise)
    if (event < 0) { event = 0 }
    net_send(event, 3)
  }
}	

PROCEDURE init_sequence (t(ms)) {
  if (number > 0) {
    on = 1
    event = t
    endt = t + 1e-6 + interval*(number-1)
  }
}

FUNCTION invl (mean (ms)) (ms) {
  if (noise == 0) {
    invl = mean
  } else {
    invl = (1. - noise)*mean + noise*mean*exprand(1)
  }
}

NET_RECEIVE (w) {
  if (flag == 0) { : external event
    if (w > 0 && on == 0) { : turn on spike sequence
      init_sequence(t)
      net_send(0, 1)
    } else if (w < 0 && on == 1) { : turn off spiking
      on = 0
    }
  }
  if (flag == 3) { : from INITIAL
    if (on == 0) {
      init_sequence(t)
      net_send(0, 1)
    }
  }
  if (flag == 1 && on == 1) {
    net_event(t)
    event = event + invl(interval)
    if (event > endt || event > end) {
      on = 0
    } else {
      net_send(event - t, 1)
    }
  }
}

FUNCTION fflag () { fflag=1 }

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