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

 Download zip file   Auto-launch 
Help downloading and running models
Accession:147141
Learning in the brain requires complementary mechanisms: potentiation and activity-dependent homeostatic scaling. We introduce synaptic scaling to a biologically-realistic spiking model of neocortex which can learn changes in oscillatory rhythms using STDP, and show that scaling is necessary to balance both positive and negative changes in input from potentiation and atrophy. We discuss some of the issues that arise when considering synaptic scaling in such a model, and show that scaling regulates activity whilst allowing learning to remain unaltered.
Reference:
1 . 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
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 L6 pyramidal corticothalamic GLU cell; Neocortex V1 L2/6 pyramidal intratelencephalic GLU cell; Neocortex V1 interneuron basket PV GABA cell; Neocortex fast spiking (FS) interneuron; Neocortex spiny stellate cell; Neocortex spiking regular (RS) neuron; Neocortex spiking low threshold (LTS) neuron; Abstract integrate-and-fire adaptive exponential (AdEx) neuron;
Channel(s):
Gap Junctions:
Receptor(s): GabaA; AMPA; NMDA;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON; Python;
Model Concept(s): Synaptic Plasticity; Long-term Synaptic Plasticity; Learning; STDP; Homeostasis;
Implementer(s): Lytton, William [bill.lytton at downstate.edu]; Neymotin, Sam [Samuel.Neymotin at nki.rfmh.org]; Rowan, Mark [m.s.rowan at cs.bham.ac.uk];
Search NeuronDB for information about:  Neocortex V1 L6 pyramidal corticothalamic GLU cell; Neocortex V1 L2/6 pyramidal intratelencephalic GLU cell; Neocortex V1 interneuron basket PV GABA cell; GabaA; AMPA; NMDA; Gaba; Glutamate;
/
stdpscalingpaper
batchscripts
mod
README
alz.hoc
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 *
geom.hoc *
graphplug.hoc *
grvec.hoc *
init.hoc *
labels.hoc *
load.hoc *
local.hoc *
makepopspikenq.hoc *
matfftpowplug.hoc *
matpmtmplug.hoc *
matpmtmsubpopplug.hoc *
matspecplug.hoc *
network.hoc *
nload.hoc *
nqpplug.hoc *
nqs.hoc *
nqsnet.hoc *
nrnoc.hoc *
params.hoc
plot.py
plotbatch.sh
plotbatchcluster.sh
powchgtest.hoc *
python.hoc *
pywrap.hoc *
redE2.hoc *
run.hoc
runsim.sh
setup.hoc *
shufmua.hoc *
sim.hoc
simctrl.hoc *
spkts.hoc *
stats.hoc *
stdpscaling.hoc
syncode.hoc *
vsampenplug.hoc *
writedata.hoc
xgetargs.hoc *
                            
// $Id: shufmua.hoc,v 1.16 2012/01/11 19:39:35 samn Exp $ 

ncells = 300
nsec = 30
binsz = 5
refrac = 2.5
jitterdt = 0
declare("reg",0)
declare("noisespks", 0)

objref vs[ncells],vh[ncells],vmua,rdm,vy,vx
vmua=new Vector()
rdm=new Random()
vy=new Vector()
vx=new Vector()

sz =  (1e3/binsz) * nsec
rate  = 1 // rate in Hz

sampr = 1e3 / binsz

objref nqp[300]

SPECTY=0
PRESM=0

//* checkrefrac(vector,refrac)
proc checkrefrac () { local i localobj v1
  v1=$o1
  for i=1,v1.size-1 {
    if(v1.x(i)-v1.x(i-1)<$2) v1.x(i) = v1.x(i) + $2
  }
}

//* myshuf(vec,nshuffles,rdm)
proc myshuf () { local idx,i,j,k,n localobj v1,rdm,vm,vr,vr2
  v1=$o1 n=$2 rdm=$o3
  vm=new Vector(v1.size)
  vm.resize(0)
  for i=0,v1.size-1 if(v1.x(i)>0) vm.append(i)
  vr=new Vector(n)
  vr2=new Vector(n)
  rdm.discunif(0,vm.size-1)
  vr.setrand(rdm)
  rdm.discunif(0,v1.size-1)
  vr2.setrand(rdm)
  for i=0,n-1 {
    idx = vm.x(vr.x(i))
    j = vr2.x(i)
    k = v1.x(idx)
    v1.x(idx) = v1.x(j)
    v1.x(j) = k
  }
}

//* applyjitter(vec,rdm,dt)
proc applyjitter () { local i,jdt localobj v1,rdm,vj
  v1=$o1 rdm=$o2 jdt=$3
  vj=new Vector(v1.size)
  rdm.uniform(-jdt,jdt)
  vj.setrand(rdm)
  v1.add(vj)
  for i=0,v1.size-1 if(v1.x(i)<0) v1.x(i)=0// make sure no neg #s
  v1.sort()
}

//* addnoise(numspikes,rdm)
proc addnoise () { local i,ns localobj vec
  ns=$1 rdm.uniform(0,nsec*1e3)
  vec=new Vector(ns)
  for i=0,ncells-1 {
    vec.setrand(rdm)
    vs[i].append(vec)
    vs[i].sort()
  }
}

//* initcells
proc initcells () { local tt,isi,i,nshuf localobj vr
  nspks =  rate * nsec
  rdm.ACG(1234*nshuf)  
  for i=0,ncells-1 if(vs[i]==nil) vs[i]=new Vector() else vs[i].resize(0)
  if(reg) {
    isi = 1e3 / rate
    tt = isi
    for i=0,nspks-1 {
      vs[0].append(tt)
      tt += isi
    }
  } else {
    vs[0].resize(nspks)
    rdm.uniform(0,nsec*1e3)
    vs[0].setrand(rdm)
    vs[0].sort()
  }
  checkrefrac(vs[0],refrac)
  for i=1,ncells-1 {
    vs[i].copy(vs[0])
    applyjitter(vs[i],rdm,jitterdt)
    checkrefrac(vs[i],refrac)
  }
  if(noisespks) addnoise(noisespks,rdm)
}

//* histcells - make spike counts per time for each cell
proc histcells () { local i
  for i=0,ncells-1 {
    if(vh[i]==nil) vh[i]=new Vector() 
    vh[i].hist(vs[i],0,sz,binsz)
  }
}

//* shufhist(nshuf)
proc shufhist () { local i,nshuf
  nshuf=$1
  for i=1,ncells-1 {
    vh[i].copy(vh[0])
    myshuf(vh[i],nshuf,rdm)
    if(refrac) checkrefrac(vh[i],refrac)
  }
}

//* mkmua
proc mkmua () { local i
  vmua.resize(vh[0].size())
  vmua.fill(0)
  for i=0,ncells-1 vmua.add(vh[i])
  vmua.sub(vmua.mean())
}

//* plotrast
proc plotrast () { local i
  vrsz(0,vx,vy)
  for i=0,ncells-1 {
    for j=0,vs[i].size-1 {
      vx.append(vs[i].x(j))
      vy.append(i)
    }
  }
  vy.mark(g,vx,"O",2,1)
  g.exec_menu("View = plot")
}

//* setjitter(jitterdt)
proc setjitter () {
  jitterdt = $1
  initcells()
  histcells()
  mkmua()
}

//* jittertest(maxjitter,jitterinc)
proc jittertest () { local i
  for(jitterdt=0;jitterdt<=$1;jitterdt+=$2) {
    print "jitterdt is " , jitterdt
    setjitter(jitterdt)
    {nqsdel(nqp[i]) nqp[i]=getspecnq(vmua,sampr,SPECTY,PRESM)}
    // nqp.gr("pow","f",0,1,1)
    i += 1
  }
}

proc nqpg () {
  nqp[$1].gr("pow","f",0,1,1)
  g.exec_menu("View = plot")
}

//* main

setjitter(0)
gg()

Loading data, please wait...