Prosthetic electrostimulation for information flow repair in a neocortical simulation (Kerr 2012)

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This model is an extension of a model (<a href="">138379</a>) recently published in Frontiers in Computational Neuroscience. This model consists of 4700 event-driven, rule-based neurons, wired according to anatomical data, and driven by both white-noise synaptic inputs and a sensory signal recorded from a rat thalamus. Its purpose is to explore the effects of cortical damage, along with the repair of this damage via a neuroprosthesis.
1 . Kerr CC, Neymotin SA, Chadderdon GL, Fietkiewicz CT, Francis JT, Lytton WW (2012) Electrostimulation as a prosthesis for repair of information flow in a computer model of neocortex IEEE Transactions on Neural Systems & Rehabilitation Engineering 20(2):153-60 [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 V1 pyramidal intratelencephalic L2-5 cell; Neocortex V1 interneuron basket PV cell; Neocortex fast spiking (FS) interneuron; Neocortex spiny stellate cell;
Channel(s): I Chloride; I Sodium; I Potassium;
Gap Junctions:
Receptor(s): GabaA; AMPA; NMDA; Gaba;
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Activity Patterns; Deep brain stimulation; Information transfer; Brain Rhythms;
Implementer(s): Lytton, William [billl at]; Neymotin, Sam [samn at]; Kerr, Cliff [cliffk at];
Search NeuronDB for information about:  Neocortex V1 pyramidal corticothalamic L6 cell; Neocortex V1 pyramidal intratelencephalic L2-5 cell; Neocortex V1 interneuron basket PV cell; GabaA; AMPA; NMDA; Gaba; I Chloride; I Sodium; I Potassium; Gaba; Glutamate;
infot.mod *
intf6_.mod *
intfsw.mod *
misc.mod *
nstim.mod *
staley.mod *
stats.mod *
vecst.mod *
decmat.hoc *
decnqs.hoc *
default.hoc *
drline.hoc *
infot.hoc *
local.hoc *
misc.h *
ratlfp.dat *
setup.hoc *
simctrl.hoc *
spkts.hoc *
staley.hoc *
stats.hoc *
stdgui.hoc *
syncode.hoc *
updown.hoc *
xgetargs.hoc *
// Run this file to run intfcol
// Version: 2011aug26 by cliffk

// Load files

// Define things
objref pop1, pop2 // For the cell populations receiving input and damage/prosthesis
ncellpops=1 // Number of populations to recieve input and damage/prosthesis
pop1=new Vector(3)
pop2=new Vector(3)
if(c_or_t==1) {
if(c_or_t==2) {
if(c_or_t==1) proswt=6.7 // Weight for cortex
if(c_or_t==2) proswt=7.1 // Weight for thalamus

// Continue loading files

objref sig1, sig2, signal, cellpop // Initialize signal
if (freq>0) {
	print "Creating stimulus..."
	// Read in stimulus from file
	ropen("ratlfp.dat") // Open file for reading
	veclen=mytstop/1000*freq // Number of elements required
	filesize=363818 // Number of lines in the file. Stupid? Yes.
	sig1 = new Vector(filesize) // Create new signal. Sigh. Stupidly there's no way to automatically load an entire text file.
	for i=0,filesize-1 sig1.x[i] = fscan() // Get one number from the file
	ropen() // Close the input file
	// Downsample
	objref p
	p = new PythonObject()
	nrnpython("import pyhoc")
	origrate=4000 // 4 kHz
	npts=origrate*mytstop/1000 // Number of elements of the original to use
	sig2=new Vector(npts) // Initialize semifinal signal
	for i=0,npts-1 sig2.x[i]=sig1.x[i] // Copy vector
	signal=p.pyhoc.downsample(sig2,origrate,freq) // Finally, compute final vector
	signal.add(-signal.min()) // Make minimum zero
	signal.div(signal.max()*1.1) // Make maximum 90%
	signal.add(0.05) // Add 5% to make bounds [5%,95%]
	// Add stimulus
	cellpop=new Vector() // Which populations will get stimulated
	for i=0,ncellpops-1 cellpop.append(pop1.x[i]) // Ditto
	whichsy=AM2 // Which synapses to stimulate
	timei=0 // Time to start the stimulus
	timef=mytstop // Time to stop the stimulus
	poisadd(signal,timei,timef,freq,cellpop,sigprct,sigwt,whichsy) // Add stimulus
	print "...done."

// Add a fake stimulus -- code doesn't work otherwise
for i=0,numcols-1 for j=0,ncellpops-1 for case(&x,pop1.x[j]) for case (&y,AM2) {
        wshock[i][x][y] = 0.001 // Something very small -- needs to exist or else the Poisson stim doesn't work
      durshock[i][x][y] = 1
     prctshock[i][x][y] = 100 // Shock everything, but weights are so low it doesn't matter
        nshock[i][x][y] = 1
      isishock[i][x][y] = 100
    startshock[i][x][y] = 1000
// Add a prosthesis
isi=10 // Shock ISI in ms -- 1000/isi is the frequency in Hz
if(b_d_p==3) {
	for i=0,numcols-1 for j=0,ncellpops-1 for case(&x,pop2.x[j]) for case (&y,AM2) {
	        wshock[i][x][y] = proswt
	      durshock[i][x][y] = 1 // Instantaneous shock
	     prctshock[i][x][y] = 100 // Shock all cells in that population
	        nshock[i][x][y] = mytstop/isi // Calculate the number of shocks
	      isishock[i][x][y] = isi // Interstimulus interval
	    startshock[i][x][y] = 1000 // Offset to show change
setshock(0) // If no argument, it resets the stimulus I believe...

// For saving LFP results
run() // Run the sim
print "Saving LFP..."
oldhz=nqLFP.cob.v.size/tstop*1000 // Original sampling rate; *1000 because tstop is in ms
newhz=200 // The new frequency to sample at, in Hz
ratio=oldhz/newhz // Calculate the ratio betwen the old and new sampling rates
npts=tstop/1000*newhz // Number of points in the resampled time seris
nlayers=nqLFP.m // Number of layers (usually 5 -- 2/3, 4, 5, 6, all)
objref tempvec // Temporary place to store NQS column as a vector
objref tempstr // Name of the NQS column being selected
objref storelfp // Create matrix to store results in
storelfp = new Matrix(npts, nlayers*numcols) // Combine layers/columns into one dimension
count=0 // Set column of storelfp to zero
for i=0,numcols-1 { // Loop over all columns
  for j=0,nlayers-1 { // Loop over all layers
    tempstr=nqLFP[i].s[j] // Get this particular NQS column header
    tempvec=nqLFP[i].getcol(tempstr.s) // Save this particular NQS column to a vector
    for k=0,npts-1 { // Loop over points in the resampled time series
      // Calculate this particular data point by downsampling and store
    count+=1 // Increase column of storelfp

// For outputting LFPs to a file
objref fobj
fobj = new File(outfn1)
storelfp.fprint(fobj,"%10.1f") // It's usually in the thousands so one d.p. should do

// For saving spike results
print "Saving spikes..."
skipsnq=0 // flag to create NQS with spike times, one per column
initAllMyNQs() // setup of NQS objects with spike/other information
objref storespikes, tmpt, tmpid, tmptype, tmpcol // Initialize vectors and matrices -- the tmp vectors are for storing parts of the NQS arrays
totalnumberofspikes=0 // Calculate the total number of spikes generated across all columns
for i=0,numcols-1 totalnumberofspikes+=snq[i].cob.v.size 
storespikes = new Matrix(totalnumberofspikes, 4) // Four columns: spike time, cell ID, cell type, and spike time
count=-1 // Initialize row count
for i=0,numcols-1 { // Loop over columns
	for j=0,snq[i].cob.v.size-1 { // Loop over spikes
		if (mod(tmpid.x[j],scale)==0) { // Only collect spikes from one out of every "scale" cells
			if (mod(count,100000)==0) {print count*100/snq[i].cob.v.size} // Print progress
			storespikes.x[count][0]=tmpt.x[j] // Store spike times
			storespikes.x[count][1]=tmpid.x[j] // Store cell number
			storespikes.x[count][2]=tmptype.x[j] // Store cell type
			storespikes.x[count][3]=tmpcol.x[j] // Store column number
storespikes.resize(count,4) // Get rid of extra zeros

// For outputting spikes to a file
objref fobj2
fobj2 = new File(outfn2)
storespikes.fprint(fobj2,"%6.0f") // All quantities are integers, so this should be fine

print "batch.hoc: done"

Kerr CC, Neymotin SA, Chadderdon GL, Fietkiewicz CT, Francis JT, Lytton WW (2012) Electrostimulation as a prosthesis for repair of information flow in a computer model of neocortex IEEE Transactions on Neural Systems & Rehabilitation Engineering 20(2):153-60[PubMed]

References and models cited by this paper

References and models that cite this paper

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