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

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Accession:141505
This model is an extension of a model (<a href="http://senselab.med.yale.edu/ModelDB/ShowModel.asp?model=138379">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.
Reference:
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;
Gene(s):
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 neurosim.downstate.edu]; Neymotin, Sam [samn at neurosim.downstate.edu]; Kerr, Cliff [cliffk at neurosim.downstate.edu];
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;
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neuroprosthesis
README
infot.mod *
intf6_.mod *
intfsw.mod *
misc.mod *
nstim.mod *
staley.mod *
stats.mod *
vecst.mod *
batch.hoc
boxes.hoc
bsmart.py
col.hoc
comparecausality.py
comparerasters.py
declist.hoc
decmat.hoc *
decnqs.hoc *
decvec.hoc
default.hoc *
drline.hoc *
filtutils.hoc
flexinput.hoc
grvec.hoc
infot.hoc *
init.hoc
intfsw.hoc
labels.hoc
local.hoc *
misc.h *
mosinit.hoc
network.hoc
nload.hoc
nqs.hoc
nqsnet.hoc
nrnoc.hoc
params.hoc
pyhoc.py
ratlfp.dat *
run.hoc
runsim
setup.hoc *
simctrl.hoc *
spkts.hoc *
staley.hoc *
stats.hoc *
stdgui.hoc *
syncode.hoc *
updown.hoc *
xgetargs.hoc *
                            
// $Id: updown.hoc,v 1.116 2010/09/09 15:56:12 samn Exp $

print "Loading updown.hoc..."

printStep=0.1
CREEP_UPDOWN=1
NOV_UPDOWN=1

{declare("test_do_graphs", 1)}
{declare("drawlr", 1, "drawth", 1,"drawscale",0,"shapesz",15,"flipit",0)}
{declare("minthreshlx", 0, "maxthreshlx",9e3*printStep)}
{declare("black",1,"red",2,"blue",3,"green",4)}

// EXAMPLE USAGE
// {gvnew(-2) panobj.read_vfile("/u/billl/nrniv/sync/data/05nov02.501/v05nov02.1000")}
// panobj.rv(7)
// vec.copy(panobj.vrtmp)
// vec.mul(-1)
// gg(vec,printStep)
// objref output
// output=updown(vec)
// output.pr(10)

// updown(vec[,nq,#CUTS,LOGCUT,MIN]) -- use updown() to find spikes
// LOC(0) PEAK(1) WIDTH(2) BASE(3) HEIGHT(4) START(5) SLICES(6) SHARP(7) INDEX(8) FILE(9)
// other options
pos_updown=1 // set to 1 to move whole curve up above 0
maxp_updown=0.95 // draw top sample at 95% of max 
minp_updown=0.05 // draw bottom sample at 5% of max
over_updown=1    // turn over and try again if nothing found
allover_updown=0 // turn over and add these locs (not debugged)
verbose_updown=0 // give messages, can also turn on DEBUG_VECST for messages from updn()
{declare("dynamic_th",0)}

//** updown - calls Vector updown function and returns NQS with spikes/bumps
obfunc updown () { local a,ii,npts,logflag,min,x,sz,midp localobj bq,cq,v1,v2,v3,bb,tl,vtmp
  if (verbose_updown) printf("MAXTIME appears to be %g (printStep=%g)\n",$o1.size*printStep,printStep)
  npts=10 // n sample locations
  tl=new List()
  bq=new NQS("LOC","PEAK","WIDTH","BASE","HEIGHT","START","SLICES","SHARP","INDEX","FILE","NESTED") 
  if (numarg()>1) cq=$o2 else cq=new NQS()
  if (cq.m!=10) { cq.resize(0) 
    cq.resize("LOC","PEAK","WIDTH","BASE","HEIGHT","START","SLICES","SHARP","INDEX","FILE","NESTED") }
  if (numarg()>2) npts=$3
  if (numarg()>3) logflag=$4 else logflag=0
  if (numarg()>4) {
    if (npts!=$o5.size) printf("Correcting npts from %d to %d\n",npts,npts=$o5.size)
    if ($o5.ismono(1)) $o5.reverse
    if (! $o5.ismono(-1)) {printf("updown: Arg 5 (%s) must be monotonic\n",$o5) return}
  }
//  if (numarg()>5) dynflag=$6 else dynflag=0

  bq.listvecs(bb)
  bq.pad(5000)
  a=allocvecs(npts+3,2e4)
  v1=mso[a+0] v2=mso[a+1] v3=mso[a+2]
  for ii=0,npts-1 tl.append(mso[ii+a+3])
  v1.copy($o1)
  if (pos_updown) {
    min=v1.min
    v1.sub(min) // make it uniformly positive
  } else min=0
  if (numarg()>4) v2.copy($o5) else {
    if(logflag==2){ //"double-log" spacing

      midp = v1.max * 0.5 //50% of max is center of log axis in vertical direction

      // 1/2 of lines btwn max and midpoint
      vtmp=new Vector()
      vtmp.indgen(2,2+npts/2-1,1)
      vtmp.log()
      vtmp.scale(maxp_updown*v1.max,midp)

      v2.append(vtmp)

      // 1/2 of lines btwn min and midpoint
      vtmp.indgen(2,2+npts/2-1,1)
      vtmp.log()
      vtmp.scale(minp_updown*v1.max,midp)

      v2.append(vtmp)

      //make sure they're monotonically increasing
      v2.sort()
      v2.reverse()

    } else {
      v2.indgen(2,2+npts-1,1)   // sampling at npts points, start at 2 to avoid log(1)=0
      if (logflag) v2.log() // log sampling
      v2.scale(-maxp_updown*v1.max,-minp_updown*v1.max) v2.mul(-1)
    }
  }

  if(dynamic_th) { //allocate memory so updown can use th
    v2.resize(v1.max()+1)
    v2.resize(0)
    printf("dynamic_th\n")
  }

  vec0.copy(v2) // <--------- whats this for? copies threshold lines to global vec0

  //v2 = thresh
  v1.updown(v2,tl,bb)

  if (pos_updown) { bq.v[1].add(min) bq.v[3].add(min) }
  cq.append(bq)
  sz=bq.size(1)
  if (allover_updown) { // do it upside down as well
    v1.mul(-1) // v2 will be upside-down
    if (pos_updown) {min=v1.min v1.sub(min)}
    v1.updown(v2,tl,bb)
    bq.v[8].add(sz) bq.v[4].mul(-1) // turn HEIGHT upside down
    cq.append(bq)
  } else if (over_updown && sz==0) { // turn it over an try again
    print "updown() checking upside-down"
    v1.mul(-1) // v2 will be upside-down
    v1.updown(v2,tl,bb)
  } 
  for case(&x,0,2,5) cq.v[x].mul(printStep)
  nqsdel(bq)
  dealloc(a)
  return cq
}

//** testupdown($o1=input vector,$2=num slices,$3=logarithmic-spaced slices,$o4=thresholds slices locations)
// runs updown on input vector and returns an NQS with detected spikes/bumps
obfunc testupdown (){ local idx,left,right,x,a localobj vtmp,output,vec
  {a=allocvecs(vec) output = new NQS() vec.copy($o1)}
  if(flipit) vec.mul(-1)
  vec.sub(vec.min)
  if(numarg()>3){
    updown(vec,output,$2,$3,$o4)
  } else if(numarg()>2){
    updown(vec,output,$2,$3)
  } else{
    updown(vec,output)
  }
  if(test_do_graphs){ if(g==nil) gg()
    gg(vec,printStep,black,3) //5 is for thick lines       
    output.v[1].mark(g,output.v,"o",shapesz,red,1) //draw circles at top of peaks
    if(drawlr) for(idx=0;idx<output.v.size;idx+=1){
      left = output.v[5].x(idx)
      right = left+output.v[2].x(idx)
      g.mark(left, vec.x(left/printStep), "t",shapesz,blue ,1)
      g.mark(right,vec.x(right/printStep),"s",shapesz,green,1)
    }       
     // draw horizontal lines for slices
    if(drawth) for vtr(&x,vec0) drline(minthreshlx,x,maxthreshlx,x,red,1)
  } 
  dealloc(a)
  return output
}

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

Adesnik H, Scanziani M (2010) Lateral competition for cortical space by layer-specific horizontal circuits. Nature 464:1155-60 [PubMed]

Binzegger T, Douglas RJ, Martin KA (2004) A quantitative map of the circuit of cat primary visual cortex. J Neurosci 24:8441-53 [PubMed]

Carnevale NT, Hines ML (2006) The NEURON Book

Cui J, Xu L, Bressler SL, Ding M, Liang H (2008) BSMART: a Matlab-C toolbox for analysis of multichannel neural time series. Neural Netw 21:1094-104 [PubMed]

Francis JT, Xu S, Chapin JK (2008) Proprioceptive and cutaneous representations in the rat ventral posterolateral thalamus. J Neurophysiol 99:2291-304 [PubMed]

Gisiger T, Boukadoum M (2011) Mechanisms Gating the Flow of Information in the Cortex: What They Might Look Like and What Their Uses may be. Front Comput Neurosci 5:1-304 [PubMed]

Hines ML, Carnevale NT (2001) NEURON: a tool for neuroscientists. Neuroscientist 7:123-35 [Journal] [PubMed]

   Spatial gridding and temporal accuracy in NEURON (Hines and Carnevale 2001) [Model]

Kamiński M, Ding M, Truccolo WA, Bressler SL (2001) Evaluating causal relations in neural systems: granger causality, directed transfer function and statistical assessment of significance. Biol Cybern 85:145-57 [PubMed]

Lefort S, Tomm C, Floyd Sarria JC, Petersen CC (2009) The excitatory neuronal network of the C2 barrel column in mouse primary somatosensory cortex. Neuron 61:301-16 [PubMed]

Lizier JT, Heinzle J, Horstmann A, Haynes JD, Prokopenko M (2011) Multivariate information-theoretic measures reveal directed information structure and task relevant changes in fMRI connectivity. J Comput Neurosci 30:85-107

Lloyd KG, Davidson L, Hornykiewicz O (1975) The neurochemistry of Parkinson's disease: effect of L-dopa therapy. J Pharmacol Exp Ther 195:453-64 [PubMed]

Lytton WW, Neymotin SA, Hines ML (2008) The virtual slice setup. J Neurosci Methods 171:309-15 [Journal] [PubMed]

   The virtual slice setup (Lytton et al. 2008) [Model]

Lytton WW, Omurtag A (2007) Tonic-clonic transitions in computer simulation. J Clin Neurophysiol 24:175-81 [PubMed]

   Tonic-clonic transitions in a seizure simulation (Lytton and Omurtag 2007) [Model]

Lytton WW, Omurtag A, Neymotin SA, Hines ML (2008) Just in time connectivity for large spiking networks Neural Comput 20(11):2745-56 [Journal] [PubMed]

   JitCon: Just in time connectivity for large spiking networks (Lytton et al. 2008) [Model]

Lytton WW, Stewart M (2005) A rule-based firing model for neural networks Int J Bioelectromagn 7:47-50

Lytton WW, Stewart M (2006) Rule-based firing for network simulations. Neurocomputing 69:1160-1164

Meyer JS, Obara K, Muramatsu K (1993) Diaschisis. Neurol Res 15:362-6 [PubMed]

Neymotin SA, Jacobs KM, Fenton AA, Lytton WW (2011) Synaptic information transfer in computer models of neocortical columns. J Comput Neurosci. 30(1):69-84 [Journal] [PubMed]

   Synaptic information transfer in computer models of neocortical columns (Neymotin et al. 2010) [Model]

Quilodran R, Gariel MA, Markov NT, Falchier A, Vezoli J, Sallet J, Anderson JC, Dehay C, Doug (2008) Strong loops in the neocortex Society for Neuroscience Abstracts 853.4

Rasche D, Rinaldi PC, Young RF, Tronnier VM (2006) Deep brain stimulation for the treatment of various chronic pain syndromes. Neurosurg Focus 21:E8

Richman JS, Moorman JR (2000) Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol 278:H2039-49 [PubMed]

Schiff ND, Giacino JT, Kalmar K, Victor JD, Baker K, Gerber M, Fritz B, Eisenberg B, Biondi T (2007) Behavioural improvements with thalamic stimulation after severe traumatic brain injury. Nature 448:600-3 [PubMed]

Schroeder CE, Mehta AD, Foxe JJ (2001) Determinants and mechanisms of attentional modulation of neural processing. Front Biosci 6:D672-84

Shipp S (2005) The importance of being agranular: a comparative account of visual and motor cortex. Philos Trans R Soc Lond B Biol Sci 360:797-814 [PubMed]

Stefani A, Lozano AM, Peppe A, Stanzione P, Galati S, Tropepi D, Pierantozzi M, Brusa L, Scar (2007) Bilateral deep brain stimulation of the pedunculopontine and subthalamic nuclei in severe Parkinson's disease. Brain 130:1596-607 [PubMed]

Stoerig P, Cowey A (1997) Blindsight in man and monkey. Brain 120 ( Pt 3):535-59 [PubMed]

Traub RD, Contreras D, Cunningham MO, Murray H, Lebeau FE, Roopun A, Bibbig A, et al (2005) A single-column thalamocortical network model exhibiting gamma oscillations, sleep spindles and epileptogenic bursts J Neurophysiol 93(4):2194-232 [Journal] [PubMed]

   A single column thalamocortical network model (Traub et al 2005) [Model]
   Collection of simulated data from a thalamocortical network model (Glabska, Chintaluri, Wojcik 2017) [Model]

Van Essen DC, Anderson CH, Felleman DJ (1992) Information processing in the primate visual system: an integrated systems perspective. Science 255:419-23 [PubMed]

Von_monakow C (1914) Die Lokalisation im Grosshirn und der Abbau der Funktion durch kortikale Herde

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 [Journal] [PubMed]

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

Dura-Bernal S, Li K, Neymotin SA, Francis JT, Principe JC, Lytton WW (2016) Restoring behavior via inverse neurocontroller in a lesioned cortical spiking model driving a virtual arm. Front. Neurosci. Neuroprosthetics 10:28 [Journal]

   Cortical model with reinforcement learning drives realistic virtual arm (Dura-Bernal et al 2015) [Model]

Dura-Bernal S, Neymotin SA, Kerr CC, Sivagnanam S, Majumdar A, Francis JT, Lytton WW (2017) Evolutionary algorithm optimization of biological learning parameters in a biomimetic neuroprosthesis. IBM Journal of Research and Development (Computational Neuroscience special issue) 61(2/3):6:1-6:14 [Journal]

   Motor system model with reinforcement learning drives virtual arm (Dura-Bernal et al 2017) [Model]

Dura-Bernal S, Zhou X, Neymotin SA, Przekwas A, Francis JT, Lytton WW (2015) Cortical Spiking Network Interfaced with Virtual Musculoskeletal Arm and Robotic Arm. Front Neurorobot 9:13 [Journal] [PubMed]

   Cortical model with reinforcement learning drives realistic virtual arm (Dura-Bernal et al 2015) [Model]

Kerr CC, Van Albada SJ, Neymotin SA, Chadderdon GL, Robinson PA, Lytton WW (2013) Cortical information flow in Parkinson's disease: a composite network-field model. Front Comput Neurosci 7:39:1-14 [Journal] [PubMed]

   Composite spiking network/neural field model of Parkinsons (Kerr et al 2013) [Model]

Neymotin SA, Chadderdon GL, Kerr CC, Francis JT, Lytton WW (2013) Reinforcement learning of 2-joint virtual arm reaching in a computer model of sensorimotor cortex Neural Computation 25(12):3263-93 [Journal] [PubMed]

   Sensorimotor cortex reinforcement learning of 2-joint virtual arm reaching (Neymotin et al. 2013) [Model]

Neymotin SA, Dura-Bernal S, Lakatos P, Sanger TD, Lytton WW (2016) Multitarget Multiscale Simulation for Pharmacological Treatment of Dystonia in Motor Cortex. Front Pharmacol 7:157 [Journal] [PubMed]

   Multitarget pharmacology for Dystonia in M1 (Neymotin et al 2016) [Model]

Neymotin SA, Lee H, Park E, Fenton AA, Lytton WW (2011) Emergence of physiological oscillation frequencies in a computer model of neocortex. Front Comput Neurosci 5:19-75 [Journal] [PubMed]

   Emergence of physiological oscillation frequencies in neocortex simulations (Neymotin et al. 2011) [Model]

Rowan MS, Neymotin SA, Lytton WW (2014) Electrostimulation to reduce synaptic scaling driven progression of Alzheimer's disease. Front Comput Neurosci 8:39 [Journal] [PubMed]

   Electrostimulation to reduce synaptic scaling driven progression of Alzheimers (Rowan et al. 2014) [Model]

(38 refs)