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 [samn at neurosim.downstate.edu]; 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: makepopspikenq.hoc,v 1.6 2010/10/11 14:16:47 samn Exp $ 


// uses batch data to make nqs with population spike info

{colW=colH=3 mytstop=1e3}

strdef strrcs
strrcs="nqsnet.hoc,65,network.hoc,125,params.hoc,112,run.hoc,53,nload.hoc,182"
rcsopen(strrcs) // load sim from RCS

mytstop=htmax=tstop=20e3

rcsopen("load.hoc",87)

objref nqspk
nqspk=new NQS("sidx","SIMTYP","DISCONCOL","col","spks","binsz","avgE","spkth")
objref vspkth,vbinsz
{vspkth=new Vector() vspkth.append(0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5)}
vbinsz=new Vector()
vbinsz.append(10,15,20,25,30,35,40,45,50)
objref nqtmp
nqtmp=new NQS(1)
nqtmp.s[0].s="E"
nqtmp.verbose=0
proc myrspks () { local i,j,k,l,Espks,th,spkth
  for i=0,nqbatch.v.size-1 {
    print "sidx " , i
    {myloadone(i) SIMTYP=nqbatch.get("SIMTY",i).x DISCONCOL=nqbatch.get("DISCONCOL",i).x}
    for vtr(&binsz,vbinsz) { 
      print "sidx " , i , " binsz " , binsz
      initAllMyNQs()
      nqtmp.v.resize(nqCTY.v[E2].size)
      for vtr(&spkth,vspkth) { th=int(spkth*col.ecells)
        for j=0,numcols-1 { nqtmp.v.fill(0)
          for col.ctt(&k) if(!ice(k)) nqtmp.v.add(nqCTY[j].v[k])
          Espks=nqtmp.select("E",">=",th)
          if(Espks>0) print Espks
          nqspk.append(i,SIMTYP,DISCONCOL,j,Espks,binsz,nqtmp.v.mean,spkth)
        }
      }
    }
  }
  nqspk.sv("/u/samn/intfcol/data/10oct10_E_SPKS_D.nqs")
}

//* prit - print stats
proc prit () { local i,bsz,th,a localobj vec
  nqspk.verbose=0 bsz=$1 th=$2 a=allocvecs(vec)
  print "totals"
  for case(&SIMTYP,0,E2,I2,-E2,&i) for DISCONCOL=0,1 {
    if(nqspk.select("SIMTYP",SIMTYP,"DISCONCOL",DISCONCOL,"spkth",th,"binsz",bsz)) {
      print "SIMTYP ",SIMTYP,"DISCONCOL ",DISCONCOL,nqspk.getcol("spks").sum
    }
  }
  print "\nper minute:"
  for case(&SIMTYP,0,E2,I2,-E2,-I2,&i) for DISCONCOL=0,1 {
    if(nqspk.select("SIMTYP",SIMTYP,"DISCONCOL",DISCONCOL,"spkth",th,"binsz",bsz)) {
      vec.resize(0) vec.copy(nqspk.getcol("spks"))
      print "SIMTYP ",SIMTYP,"DISCONCOL ",DISCONCOL,"E:",3*vec.mean,"per minute"
    }
  }
  dealloc(a)
  nqspk.verbose=1
}

// time("myrspks()") // 24.090833 m


Loading data, please wait...