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

 Download zip file   Auto-launch 
Help downloading and running models
Accession:154096
"... As cells die and synapses lose their drive, remaining cells suffer an initial decrease in activity. Neuronal homeostatic synaptic scaling then provides a feedback mechanism to restore activity. ... The scaling mechanism increases the firing rates of remaining cells in the network to compensate for decreases in network activity. However, this effect can itself become a pathology, ... Here, we present a mechanistic explanation of how directed brain stimulation might be expected to slow AD progression based on computational simulations in a 470-neuron biomimetic model of a neocortical column. ... "
Reference:
1 . Rowan MS, Neymotin SA, Lytton WW (2014) Electrostimulation to reduce synaptic scaling driven progression of Alzheimer's disease. Front Comput Neurosci 8:39 [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; 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; Python;
Model Concept(s): Long-term Synaptic Plasticity; Aging/Alzheimer`s; Deep brain stimulation; Homeostasis;
Implementer(s): Lytton, William [billl at neurosim.downstate.edu]; Neymotin, Sam [samn at neurosim.downstate.edu]; Rowan, Mark [m.s.rowan at cs.bham.ac.uk];
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; Glutamate;
/
RowanEtAl2014
batchscripts
mod
README
alz.hoc
alzinfo.m
autotune.hoc *
basestdp.hoc *
batch.hoc *
batch2.hoc *
batchcommon
checkirreg.hoc *
clusterrun.sh
col.dot *
col.hoc *
comppowspec.hoc *
condisconcellfig.hoc *
condisconpowfig.hoc *
declist.hoc *
decmat.hoc *
decnqs.hoc *
decvec.hoc *
default.hoc *
drline.hoc *
e2hubsdisconpow.hoc *
e2incconpow.hoc *
filtutils.hoc *
flexinput.hoc
geom.hoc *
graphplug.hoc *
grvec.hoc *
infot.hoc *
init.hoc *
labels.hoc *
load.hoc *
local.hoc *
makepopspikenq.hoc *
matfftpowplug.hoc *
matpmtmplug.hoc *
matpmtmsubpopplug.hoc *
matspecplug.hoc *
mosinit.hoc
network.hoc *
nload.hoc *
nqpplug.hoc *
nqs.hoc *
nqsnet.hoc *
nrnoc.hoc *
params.hoc
plot.py
plotavg.py
plotbatch.sh
plotbatchcluster.sh
plotdeletions.py
plotntes.py
powchgtest.hoc *
pyhoc.py
python.hoc *
pywrap.hoc *
ratlfp.dat *
redE2.hoc *
run.hoc
runsim.sh
setup.hoc *
shufmua.hoc *
sim.hoc
simctrl.hoc *
spkts.hoc *
stats.hoc *
syncode.hoc *
vsampenplug.hoc *
writedata.hoc
xgetargs.hoc *
                            
// $Id: drline.hoc,v 1.41 2011/02/15 14:05:02 billl Exp $

print "Loading drline.hoc..."

// click and drag left button to draw lines on top of a figure interactively
// select graph to draw on with setdrl(Graph[])
// set color with clr, line width with lne
// select 'Draw curve' for continuous drawing
// select 'Arrow' to place an arrow pointing according to direction of drag

drlflush=1 //whether to flush line drawings each drline call

//* drline(x0,y0,x1,y1,OPT graph or color) 
proc drline () { local color,line
  if (numarg()==0) { print "drline(x0,y0,x1,y1[,g,col,line])"
    return }
  if (numarg()>4) { 
    if (argtype(5)==0) { color=$5 
                         if (numarg()>5) line=$6
    } else {             graphItem = $o5 
                         if (numarg()>5) color=$6
                         if (numarg()>6) line=$7      }}
  graphItem.beginline(color,line)
  graphItem.line($1,$2)
  graphItem.line($3,$4)
  if(drlflush) graphItem.flush()
}

//* set to drawlines on top of fig
proc setdrl () {
  g=$o1 // select this graph for further drawing
  xpanel("")
  $o1.menu_tool("Draw line","drl")
  $o1.menu_tool("Draw curve","drc")
  $o1.menu_tool("Label","drw")
  $o1.menu_tool("Arrow","dra")
  $o1.menu_tool("Circle","drci")
  $o1.menu_tool("Rectangle","drr")
  xvalue("Color","clr",1,"",1)
  xvalue("Line","lne",1,"",1)
  xbutton("Erase","g.erase_all()")
  xpanel()
  $o1.exec_menu("Draw line")
}

//* draw line interactively on top of fig
// interesting that this should work at all since x0,y0 local but still preserving their
// values across multiple calls
proc drl ()  { local x0,y0,type,x,y,keystate
  type=$1 x=$2 y=$3 keystate=$4
  if (type==2) {x0=x y0=y}
  if (type==3) drline(x0,y0,x,y,clr,lne)
}

//* draw circle interactively on top of fig
// drci(2,0,0,0) drci(3,1,0,0)
proc drci ()  { local a,x0,y0,type,x,y,keystate,ii,rad localobj xv,yv
  type=$1 x=$2 y=$3 keystate=$4
  if (type==2) {x0=x y0=y}
  if (type==3) { rad=sqrt((x-x0)^2+(y-y0)^2) 
    a=allocvecs(xv,yv) vrsz(360,xv,yv)
    print "Circle: ",x0,y0,rad
    yv.circ(xv,x0,y0,rad)
    yv.line(g,xv,clr,lne)
    dealloc(a)
  }
}

//* draw retangle interactively on top of fig
proc drr ()  { local x0,y0,type,x,y,keystate
  type=$1 x=$2 y=$3 keystate=$4
  if (type==2) {x0=x y0=y}
  if (type==3) { drline(x0,y0,x0,y,clr,lne)
    drline(x,y0,x,y,clr,lne) drline(x,y,x0,y,clr,lne) drline(x,y0,x0,y0,clr,lne) }
}

//* draw arrow interactively on top of fig
proc dra ()  { local xsz,ysz,type,x,y,keystate,rot
  type=$1 x=$2 y=$3 keystate=$4
  xsz=0.1*(g.size(2)-g.size(1)) // 10% of size
  ysz=0.1*(g.size(4)-g.size(3))
  if (type==2) {x0=x y0=y}
  if (type==3) {
    if (y==y0) {
      if (x>x0) rot=-90 else rot=90
    } else {
      rot=-atan((x-x0)/(y-y0))/2/PI*360
      if ((y-y0)<=0) rot+=180
    }
    g.glyph(arrow(),x,y,xsz,ysz,rot)
  }
}

//* draw curve interactively on top of fig
proc drc ()  { local x0,y0,type,x,y,keystate
  type=$1 x=$2 y=$3 keystate=$4
  if (type==2) { x0=x y0=y
  } else if (type==1) {
    drline(x0,y0,x,y,clr,lne)
    x0=x y0=y
  } else if (type==3) drline(x0,y0,x,y,clr,lne)
}

//* write label
proc drw ()  { local x0,y0,type,x,y,keystate
  type=$1 x=$2 y=$3 keystate=$4
  if (type==2) { 
   string_dialog("Label: ",tstr) 
   g.label(x,y,tstr,1,1,0.5,0.5,clr)
  }
}

obfunc arrow () { localobj o
  o=new Glyph()
  o.m(0,0)  o.l(0,2) o.s(1,4) // draw vertical line
  o.m(0,0)  o.l(0,-2) o.s(1,4) // draw vertical line
  o.m(0,-2) o.l(-2,0) o.s(1,4)
  o.m(0,-2) o.l(2,0) o.s(1,4)
  return o
}

//* hist(g,vec,min,max,bins)
{clr=1 hflg=1 ers=1 sym=1 pflg=0 lin=4 hbup=0} 
declared("hfunc")
// clr:color, hflg=1 draw lines; 2 draw boxes; 3 fill in; ers=erase; 
// pflg=1 normalize hist by size of $o2, so will be probability instead of count
// pflg=2 turn hist upside down
// pflg=3 operate on values with hfunc()
// style determined by hflg
// hflg==0 lines with dots
// hflg==0.x offset lines with dots
// hflg==1 outlines but not down to zero
// hflg==2 outlines with lines down to zero
// hflg==3 just dots
// hflg==3.x lines between dots
// hbup=1 // move baseline up by this amount
func hist () { local a,b,c,min,max,wid,bins,ii,jj,offset,x,y
  if (numarg()==0) { printf("hist(g,vec,min,max,bins)\n") return 0}
  if ($o2.size<2)  { printf("hist: $o2 too small\n",$o2) return -1}
  if ($o2.min==$o2.max)  { printf("hist: %s all one value: %g\n",$o2,$o2.min) return -1}
  if (numarg()==5) {min=$3 max=$4 bins=$5 
  } else if (numarg()==4) { min=0 max=$3 bins=$4 
  } else if (numarg()<=3) { 
    if ((min=0.95*$o2.min)<0) min=1.05*$o2.min
    if ((max=1.05*$o2.max)<0) max=0.95*$o2.max
    bins=100
    if (min>0) min*=0.9 else min*=1.1
    if (max>0) max*=1.1 else max*=0.9
    if (numarg()==3) bins=$3
  }
  wid=(max-min)/bins
  // print min,max,max-wid,wid
  a=b=c=allocvecs(3) b+=1 c+=2
  offset=0 x=-1
  if (ers) $o1.erase_all()
  mso[c].hist($o2,min,bins,wid) // c has values
  if(pflg==1) mso[c].div(mso[c].sum) // normalize to sum to 1
  if(pflg==2) mso[c].mul(-1)
  if(pflg==3) hfunc(mso[c])
  mso[a].resize(2*mso[c].size())
  mso[a].indgen(0.5) 
  mso[a].apply("int") 
  mso[b].index(mso[c], mso[a]) 
  mso[a].mul(wid) mso[a].add(min)
  mso[b].rotate(1)
  mso[b].x[0] = 0 
  mso[b].append(mso[b].x[mso[b].size-1],0)
  mso[b].add(hbup)
  mso[a].append(max,max)
  if (hflg==1 || hflg==2) { 
    mso[b].line($o1, mso[a],clr,lin)
    if (hflg==2) for vtr(&x,mso[a]) drline(x,0,x,mso[b].x[i1],$o1,clr,lin)
  } else if (int(hflg)==0 || hflg>=3) { 
    if (hflg%1!=0) offset=hflg*wid // use eg -0.5+ii/8 to move back to integer
    mso[a].indgen(min,max-wid,wid)
    mso[a].add(wid/2+offset)
    // print mso[a].min,mso[a].max
    // mso[c].mark($o1,mso[a],"O",6,clr,2) // this will place points where 0 count
    for jj=0,mso[a].size-1 if (mso[c].x[jj]!=0) {
      if (hflg!=3 && hflg%1!=0) drline(mso[a].x[jj],0,mso[a].x[jj],mso[c].x[jj],$o1,clr,lin)
      if (hflg==4) {
        if (x!=-1) drline(x,y,mso[a].x[jj],mso[c].x[jj],$o1,clr,lin)
        x=mso[a].x[jj] y=mso[c].x[jj]
      }
      $o1.mark(mso[a].x[jj],mso[c].x[jj],sg(sym).t,10,clr,2) // don't place points with 0 count
    }
  }
  $o1.flush()
  $o1.size(min,max,0,mso[b].max)
  dealloc(a)
  return 1
}

// barplot(g,yvec,xvec[,bar_width]) 
// barplot(g,yvec,xvec[,bar_width,color_vec]) -- for multicolored bars -- each point has a color
// barplot(g,yvec,xvec[,bar_width,color_vec,error_vec]) -- error_vec plots the error
scribble=0
func barplot () { local a,sz,wid,ii,jj,x,y,mulcol localobj go,vx,vy,v1,vcol
  if (numarg()==0) {
    printf("barplot(g,yvec,xvec[,bar_width]), scribble=1 to 'fill in'\n") 
    printf("set scribble=1 to fill in with single color (based on clr)\n")
    printf("barplot(g,yvec,xvec[,bar_width,color_vec]):multicolored bars-each point has a color\n")
    printf("barplot(g,yvec,xvec[,bar_width,color_vec,error_vec]):add +/- error to each bar\n")
    return 0}
  if ((sz=$o2.size)!=$o3.size)  { printf("barplot: x,y vectors differ in size\n") return -1}
  go=$o1 $o3.sort
  if (argtype(4)==0)  wid=$4 else wid=1
  if (argtype(5)==1)  {vcol=$o5 mulcol=-1
    if (sz!=vcol.size) { printf("barplot: color vec wrong size: %d %d\n",sz,vcol.size) return -1}  
  } else if (argtype(5)==0) mulcol=$5 else mulcol=0
  wid/=2
  // print min,max,max-wid,wid
  a=allocvecs(vx,vy,v1)
  if (ers) go.erase_all()
  for vtr2(&x,&y,$o3,$o2,&ii)  { 
    vx.append(x-wid,x-wid,x+wid,x+wid)
    vy.append(0,y,y,0)
  }
  if (mulcol) {
    for vtr2(&x,&y,$o3,$o2,&jj)  { 
      if (mulcol==-1) clr=vcol.x[jj] else clr=mulcol
      vrsz(0,vx,vy)
      vx.append(x-wid,x-wid)
      vy.append(0,y)
      for (ii=0;ii<2*wid;ii+=(wid/100)) { 
        vx.add(wid/100) 
        vy.line(go, vx, clr, 4)
      }
    }
    vy.line(go, vx, clr, 4)
  } else if (scribble) {
    vrsz(0,vx,vy)
    for vtr2(&x,&y,$o3,$o2,&ii)  { 
      vx.append(x-wid,x-wid,x-wid)
      vy.append(0,y,0)
    }
    for (ii=0;ii<2*wid;ii+=(wid/100)) { 
      vx.add(wid/100) 
      vy.line(go, vx, clr, 4)
    }
    vy.line(go, vx, clr, 4)
  } else vy.line(go, vx, clr, lne)
  if(numarg()>5) $o2.ploterr(go, $o3, $o6, 15, 1, 3)
  go.flush()
  go.size(vx.min-wid,vx.max+wid,0,vy.max)
  dealloc(a)
  return 1
}

proc smgs () { local a,b,c,min,max,wid,bins,ii,jj,offset,x,y localobj v1
  if ($o2.size<2)  { printf("smgs: $o2 too small\n",$o2) return -1}
  if ($o2.min==$o2.max)  { printf("smgs: %s all one value: %g\n",$o2,$o2.min) return -1}
  if (numarg()==5) {min=$3 max=$4 bins=$5 
  } else if (numarg()==4) { min=0 max=$3 bins=$4 
  } else if (numarg()<=3) { 
    if ((min=0.95*$o2.min)<0) min=1.05*$o2.min
    if ((max=1.05*$o2.max)<0) max=0.95*$o2.max
    bins=100
    if (min>0) min*=0.9 else min*=1.1
    if (max>0) min*=1.1 else max*=0.9
    if (numarg()==3) bins=$3
  }
  wid=(max-min)/bins
  // print min,max,max-wid,wid
  a=b=c=allocvecs(3,1e4) b+=1 c+=2
  offset=0 x=-1
  if (ers) $o1.erase_all()
  mso[a].indgen(min,max,wid)
  if (0) {
    mso[c].smgs($o2,min,max,wid,wid*wid/4) // c has values
    mso[c].line($o1, mso[a],clr,4)
  } else {
    v1=$o2.sumgauss(min,max,wid,wid/2) // c has values
    v1.line($o1, mso[a],clr,4)
  }
}

//* a few drawing utilities from sam (not too spectacular)
 
//** drawhticks(ticksz,minx,maxx,linewidth,$5-$numarg() == y position of horizontal ticks)
// draw horizontal ticks of a view box along left/right of box
proc drawhticks () { local ticksz,minx,maxx,lw,i
  ticksz=$1 minx=$2 maxx=$3 lw=$4
  for i=5,numarg() {
    drline(minx,$i,minx+ticksz,$i,g,1,lw)    drline(maxx,$i,maxx-ticksz,$i,g,1,lw)
  }
}

//** drawvticks(ticksz,miny,maxy,linewidth,$5-$numarg() == x position of vertical ticks)
// draw vertical ticks of a view box along top/bottom of box
proc drawvticks () { local ticksz,miny,maxy,lw,i
  ticksz=$1 miny=$2 maxy=$3 lw=$4
  for i=5,numarg() {
    drline($i,miny,$i,miny+ticksz,g,1,lw)    drline($i,maxy,$i,maxy-ticksz,g,1,lw)
  }
}

//** drawbox(minx,maxx,miny,maxy[,line,graph]) - draw box
proc drawbox () { local minx,maxx,miny,maxy,ln localobj myg
  minx=$1 maxx=$2 miny=$3 maxy=$4
  if(numarg()>4)ln=$5 else ln=3
  if(numarg()>5)myg=$o6 else myg=g
  drline(minx,miny,minx,maxy,myg,1,ln) //bottom
  drline(minx,miny,maxx,miny,myg,1,ln) //left
  drline(minx,maxy,maxx,maxy,myg,1,ln) //top
  drline(maxx,miny,maxx,maxy,myg,1,ln) //right
}

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

References and models cited by this paper

References and models that cite this paper

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

Busche MA, Eichhoff G, Adelsberger H, Abramowski D, Wiederhold KH, Haass C, Staufenbiel M, Ko (2008) Clusters of hyperactive neurons near amyloid plaques in a mouse model of Alzheimer's disease. Science 321:1686-9

Carnevale NT, Hines ML (2006) The NEURON Book

Chandler B, Grossberg S (2012) Joining distributed pattern processing and homeostatic plasticity in recurrent on-center off-surround shunting networks: noise, saturation, short-term memory, synaptic scaling, and BDNF. Neural Netw 25:21-9

Crumiller M, Knight B, Yu Y, Kaplan E (2011) Estimating the amount of information conveyed by a population of neurons. Front Neurosci 5:90-31 [PubMed]

Demuro A, Parker I, Stutzmann GE (2010) Calcium signaling and amyloid toxicity in Alzheimer disease. J Biol Chem 285:12463-8

Frohlich F, Bazhenov M, Sejnowski TJ (2008) Pathological effect of homeostatic synaptic scaling on network dynamics in diseases of the cortex. J Neurosci 28:1709-20 [PubMed]

Gourevitch B, Eggermont JJ (2007) Evaluating information transfer between auditory cortical neurons. J Neurophysiol 97:2533-43 [PubMed]

Hansen N (2012) Action mechanisms of transcranial direct current stimulation in Alzheimer's disease and memory loss. Front Psychiatry 3:48-8

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 [Journal] [PubMed]

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

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]

Liebetanz D, Koch R, Mayenfels S, Konig F, Paulus W, Nitsche MA (2009) Safety limits of cathodal transcranial direct current stimulation in rats. Clin Neurophysiol 120:1161-7

Lytton WW, Neymotin SA, Kerr CC (2014) Multiscale modeling for clinical translation in neuropsychiatric disease J Comput Surg 1:7

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

Modolo J, Beuter A (2009) Linking brain dynamics, neural mechanisms, and deep brain stimulation in Parkinson's disease: an integrated perspective. Med Eng Phys 31:615-23

Neymotin S, Kerr C, Francis J, Lytton W (2011) Training oscillatory dynamics with spike-timing-dependent plasticity in a computer model of neocortex Signal Processing in Medicine and Biology Symposium (SPMB), IEEE :1-6

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]

Palop JJ, Mucke L (2010) Amyloid-beta-induced neuronal dysfunction in Alzheimer's disease: from synapses toward neural networks. Nat Neurosci 13:812-8 [PubMed]

Qiu S, Anderson CT, Levitt P, Shepherd GM (2011) Circuit-specific intracortical hyperconnectivity in mice with deletion of the autism-associated Met receptor tyrosine kinase. J Neurosci 31:5855-64

Rabey JM, Dobronevsky E, Aichenbaum S, Gonen O, Marton RG, Khaigrekht M (2013) Repetitive transcranial magnetic stimulation combined with cognitive training is a safe and effective modality for the treatment of Alzheimer's disease: a randomized, double-blind study. J Neural Transm 120:813-9

Rowan MS,Neymotin SA (2013) Synaptic Scaling Balances Learning in a Spiking Model of Neocortex Adaptive and Natural Computing Algorithms, Tomassini M, Antonioni A, Daolio F, Buesser P, ed. pp.20 [Journal]

   Synaptic scaling balances learning in a spiking model of neocortex (Rowan & Neymotin 2013) [Model]

Rutherford LC, Nelson SB, Turrigiano GG (1998) BDNF has opposite effects on the quantal amplitude of pyramidal neuron and interneuron excitatory synapses. Neuron 21:521-30 [PubMed]

Savioz A, Leuba G, Vallet PG, Walzer C (2009) Contribution of neural networks to Alzheimer disease's progression. Brain Res Bull 80:309-14

Shepherd GM (2013) Corticostriatal connectivity and its role in disease. Nat Rev Neurosci 14:278-91

Small DH (2008) Network dysfunction in Alzheimer's disease: does synaptic scaling drive disease progression? Trends Mol Med 14:103-8 [PubMed]

Smith GS, Laxton AW, Tang-Wai DF, McAndrews MP, Diaconescu AO, Workman CI, Lozano AM (2012) Increased cerebral metabolism after 1 year of deep brain stimulation in Alzheimer disease. Arch Neurol 69:1141-8 [PubMed]

Trasande CA, Ramirez JM (2007) Activity deprivation leads to seizures in hippocampal slice cultures: is epilepsy the consequence of homeostatic plasticity? J Clin Neurophysiol 24:154-64 [PubMed]

Turrigiano G (2011) Too many cooks? Intrinsic and synaptic homeostatic mechanisms in cortical circuit refinement. Annu Rev Neurosci 34:89-103 [PubMed]

Turrigiano GG (2008) The self-tuning neuron: synaptic scaling of excitatory synapses. Cell 135:422-35 [PubMed]

Turrigiano GG, Leslie KR, Desai NS, Rutherford LC, Nelson SB (1998) Activity-dependent scaling of quantal amplitude in neocortical neurons. Nature 391:892-6 [PubMed]

Utz KS, Dimova V, Oppenlander K, Kerkhoff G (2010) Electrified minds: transcranial direct current stimulation (tDCS) and galvanic vestibular stimulation (GVS) as methods of non-invasive brain stimulation in neuropsychology--a review of current data and future implications. Neuropsychologia 48:2789-810

van Rossum MC, Bi GQ, Turrigiano GG (2000) Stable Hebbian learning from spike timing-dependent plasticity. J Neurosci 20:8812-21 [PubMed]

Weiler N, Wood L, Yu J, Solla SA, Shepherd GM (2008) Top-down laminar organization of the excitatory network in motor cortex. Nat Neurosci 11:360-6 [Journal] [PubMed]

   Laminar connectivity matrix simulation (Weiler et al 2008) [Model]

Yu Y, Crumiller M, Knight B, Kaplan E (2010) Estimating the amount of information carried by a neuronal population. Front Comput Neurosci 4:10-810

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]

Neymotin SA, McDougal RA, Sherif MA, Fall CP, Hines ML, Lytton WW (2015) Neuronal calcium wave propagation varies with changes in endoplasmic reticulum parameters: a computer model Neural Computation 27(4):898-924 [Journal] [PubMed]

   Neuronal dendrite calcium wave model (Neymotin et al, 2015) [Model]

(38 refs)