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
Citations  Citation Browser
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 [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 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: e2hubsdisconpow.hoc,v 1.1 2010/10/11 03:25:34 samn Exp $ 


// draw panels showing power spectra for disconnected columns at baseline comparing with 10 E2 hubs

if(!name_declared("nqbatch")) rcsopen("load.hoc",87)

if(g==nil) gg()

declare("nqf","o[2]","sidx",0,"nqfs","o[2]","winsz",201,"stdg",0.5,"lc",new List())
{lc.append(new String("C%dintraE")) lc.append(new String("C%dintraI"))}
declare("vsidx",new Vector())
{nqbatch.select(-1,"ISEED",1234,"DVSEED",534023,"SIMTYP",0,"DISCONCOL",1) vsidx.append(nqbatch.ind.x(0))}
{nqbatch.select(-1,"ISEED",1234,"DVSEED",534023,"SIMTYP",18,"DISCONCOL",1) vsidx.append(nqbatch.ind.x(0))}

for vtr(&sidx,vsidx,&i) {
  nqf[i]=nqbatch.get("nqpmtm",sidx).o
  nqfs[i]=new NQS()
  nqfs[i].cp(nqf[i])
}

//* drit(code,filt)
proc drit () { local i,j,cc
  if($1>=0 && $1<=1) {
    for i=0,1 for j=0,numcols-1 {
      sprint(tstr,lc.o($1).s,j)
      cc=nqfs[i].fi(tstr)
      nqfs[i].v[cc].copy(nqf[i].v[cc])
      myfilt($2,nqfs[i].v[cc])
      nqfs[i].gr(tstr,"f",0,i+1,1)
    }
  }
}