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

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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 cell; Neocortex V1 L2/6 pyramidal intratelencephalic 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; 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 [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 L6 pyramidal corticothalamic cell; Neocortex V1 L2/6 pyramidal intratelencephalic cell; Neocortex V1 interneuron basket PV cell; GabaA; AMPA; NMDA; Gaba; Glutamate;
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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: comppowspec.hoc,v 1.11 2011/02/22 21:02:34 samn Exp $ 


// performs comparisons of experimental to simulation power spectra

{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",88)

if(g==nil)gg()

objref nqe // experimental data power spectra
nqe=new NQS("/u/samn/ibohk/data/10sep24_sal-j6-0611-2.bpf_matfftpow_smooth.nqs")
//nqe=new NQS("/u/samn/ibohk/data/10sep24_sal-j6-0611-2.bpf_matfftpow_raw.nqs")

declare("sixcut",0) // do a six Hz cutoff

objref nqe2
nqe2=new NQS()
nqe.select("f","<=",100) // 209716
nqe2.cp(nqe.out) 

objref nqe2rs // resample power spectra to have same size as nqf200 (or nqpmtm)
strdef strpow
strpow="nqf200"
proc mknqe2rs () {
  {nqsdel(nqe2rs) nqe2rs=new NQS("f","pow")}
  if(!strcmp(strpow,"nqf200")) nsz=2048 else nsz=2049
  for i=0,1 { // do the resampling
    nqe2rs.v[i].copy(nqe2.v[i])
    resample(nqe2rs.v[i],nsz)
  }  
  mx=nqe2rs.v[1].max // peak amplitude in experimental data
}

objref nqn,vfctr
{vfctr=new Vector() vfctr.indgen(0.05,1,0.05)}

fctr=1

//* mknqn - make an nqs with error to find optimal matches
proc mknqn () { local sc
  {mknqe2rs() nqsdel(nqn) nqn=new NQS("sidx","SIMTYP","col","err","fctr","DISCONCOL")}
  for i=0,nqbatch.v.size-1 {
    print i
    nq=nqbatch.get(strpow,i).o
    sidx=nqbatch.get("sidx",i).x
    SIMTYP=nqbatch.get("SIMTYP",i).x
    DISCONCOL=nqbatch.get("DISCONCOL",i).x
    for j=0,numcols-1 {
      {sprint(tstr,"C%dintraE",j) vec.resize(0) vec.copy(nq.v[nq.fi(tstr)])}

      if(sixcut) vec.fill(0,0,62*2)
      if(!strcmp(strpow,"nqpmtm")) boxfilt(vec,201)

      for vtr(&fctr,vfctr) {
        vec0.copy(vec)
        if(sixcut) vec0.fill(0,0,62)
        if(mx>vec0.max) sc=fctr*mx/vec0.max else sc=fctr*vec0.max/mx
        vec0.mul(sc)
        nqn.append(sidx,SIMTYP,j,vec0.meansqerr(nqe2rs.v[1]),fctr,DISCONCOL)
      }
    }
  }
}

objref myv[5]
//* drit(exclude disconcol) - draw the best matches
proc drit () { local skipdiscon
  if(numarg()>0)skipdiscon=$1 else skipdiscon=1
  nqe2.gr("pow","f",0,1,1)  
  for case(&SIMTYP,0,18,20,-18,&i) {
    myv[i]=new Vector()
    if(skipdiscon) nqn.select("SIMTYP",SIMTYP) else nqn.select("SIMTYP",SIMTYP,"DISCONCOL",0)
    err=nqn.getcol("err").min
    nqn.select("SIMTYP",SIMTYP,"err",err)
    nq=nqbatch.get(strpow,nqn.fetch("sidx")).o
    vec.resize(0)
    sprint(tstr,"C%dintraE",nqn.fetch("col"))
    vec.copy(nq.v[nq.fi(tstr)])
    fctr=nqn.fetch("fctr")
    if(sixcut) vec.fill(0,0,62*2)
    if(!strcmp(strpow,"nqpmtm")) boxfilt(vec,201)
    {myv[i].copy(vec) myv[i].mul(fctr*mx/myv[i].max)}  
    myv[i].plot(g,nq.v,i+2,1)
    print SIMTYP,err
  }
}

//* drbad(exclude disconcol) - draw the worst matches
proc drbad () { local skipdiscon,idx
  if(numarg()>0)skipdiscon=$1 else skipdiscon=1
  nqe2.gr("pow","f",0,1,1)  
  for case(&SIMTYP,0,18,20,-18,&i) {
    myv[i]=new Vector()
    if(skipdiscon) nqn.select("SIMTYP",SIMTYP) else nqn.select("SIMTYP",SIMTYP,"DISCONCOL",0)
    err=nqn.getcol("err").max
    
    nqn.select("SIMTYP",SIMTYP,"err",err)
//    print nqn.select("SIMTYP",SIMTYP,"err",">=",err*.9)
//    for vtr(&idx,nq
    nq=nqbatch.get(strpow,nqn.fetch("sidx")).o
    vec.resize(0)
    sprint(tstr,"C%dintraE",nqn.fetch("col"))
    vec.copy(nq.v[nq.fi(tstr)])
    fctr=nqn.fetch("fctr")
    if(sixcut) vec.fill(0,0,62*2)
    if(!strcmp(strpow,"nqpmtm")) boxfilt(vec,201)
    {myv[i].copy(vec) myv[i].mul(fctr*mx/myv[i].max)}  
    myv[i].plot(g,nq.v,i+2,1)
    print SIMTYP,err
  }
}


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