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 V1 pyramidal corticothalamic L6 cell; Neocortex M1 pyramidal intratelencephalic L2-6 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 [billl at neurosim.downstate.edu]; Neymotin, Sam [samn at neurosim.downstate.edu]; 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 V1 pyramidal corticothalamic L6 cell; Neocortex M1 pyramidal intratelencephalic L2-6 cell; GabaA; AMPA; NMDA; Gaba; Glutamate;
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src
README
infot.mod *
intf6.mod *
intf6.mod'A=0
intfsw.mod *
intfsw.mod'A=0
matrix.mod
misc.mod *
nstim.mod *
staley.mod *
stats.mod *
vecst.mod *
boxes.hoc *
col.hoc
declist.hoc *
decmat.hoc *
decmat.hoc'
decnqs.hoc *
decnqs.hoc'A=0
decvec.hoc *
default.hoc *
drline.hoc *
filtutils.hoc *
gcelldata.hoc
gmgs102.nqs
grvec.hoc *
infot.hoc *
init.hoc
intfsw.hoc *
intfsw.hoc'A=0
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 *
spkts.hoc'A=0
staley.hoc *
stats.hoc *
stdgui.hoc *
syncode.hoc *
updown.hoc *
wdmaps2.nqs
xgetargs.hoc *
                            
// SAVEWEIGHTS
// This code saves the weights of a sampling of 
// synapses at regular intervals. It's based on arm.hoc.
// Version: 2012jun23

timebetweensaves=0.5 // Interval between saves, in s

//* savesynapticweights - save synaptic weights from L5 cells
objref nqsy
nqsy=new NQS("id1","id2","wg","t")
proc savesynapticweights () { local i,a localobj xo,vwg,vtau,vinc,vmaxw,vidx,vt,vpre
  a=allocvecs(vidx,vwg,vtau,vinc,vmaxw,vt,vpre)
  for ltr(xo,col[0].ce) {
      vrsz(xo.getdvi(vidx),vwg,vtau,vinc,vmaxw,vt,vpre)
      xo.getplast(vwg,vtau,vinc,vmaxw) // get current weight gains    
      vt.fill(t) //time
      vpre.fill(xo.id) //presynaptic id
      nqsy.v[0].append(vpre) 
      nqsy.v[1].append(vidx)
      nqsy.v[2].append(vwg)
      nqsy.v[3].append(vt)
  }
  dealloc(a)
}

// Initialize all the events; one save per second
proc periodicweightsave() { local sec
	for thissave=0,int(mytstop/timebetweensaves/1000)-1 cvode.event(thissave*timebetweensaves*1000,"savesynapticweights()")
}

// Set up actual events to happen
objref weightshandler
weightshandler = new FInitializeHandler(1,"periodicweightsave()")

// Procedure for saving weights to disk. Based on code from saveoutput.hoc.
strdef outfnweights
sprint(outfnweights,"%s-wts.txt",filestem) // Store the connectivity information
proc writeweightstodisk() { local i,j localobj conpreid, conpostid, conweight, contime, dynamicweights, fobjweights
	// For saving cell connectivity
	print "Saving dynamic connectivity..."
	conpreid=nqsy.getcol("id1")
	conpostid=nqsy.getcol("id2")
	conweight=nqsy.getcol("wg")
	contime=nqsy.getcol("t")
	// Initialize array
	n = conpreid.size()
	print "  Number of connections: ", n
	dynamicweights = new Matrix(n,4) // PreID, post ID, weight, time
	for i=0,n-1 { // Loop over each synapse
		dynamicweights.x[i][0]=contime.x[i]
		dynamicweights.x[i][1]=conpreid.x[i]
		dynamicweights.x[i][2]=conpostid.x[i]
		dynamicweights.x[i][3]=conweight.x[i]
		}
	// Save results to disk in text format
	print "  Saving to file..."
	fobjweights = new File(outfnweights)
	fobjweights.wopen()
	dynamicweights.fprint(0,fobjweights,"%8.2f") // Not sure how many sig figs to have
	fobjweights.close()
	print "  ...done..."
}

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