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

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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.
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 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; Abstract integrate-and-fire adaptive exponential (AdEx) neuron;
Gap Junctions:
Receptor(s): GabaA; AMPA; NMDA;
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]; Neymotin, Sam [samn at]; Rowan, Mark [m.s.rowan at];
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;
autotune.hoc *
basestdp.hoc *
batch.hoc *
batch2.hoc *
checkirreg.hoc * *
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 *
powchgtest.hoc *
python.hoc *
pywrap.hoc *
redE2.hoc *
setup.hoc *
shufmua.hoc *
simctrl.hoc *
spkts.hoc *
stats.hoc *
syncode.hoc *
vsampenplug.hoc *
xgetargs.hoc *
// $Id: stats.hoc,v 1.6 2011/07/05 20:31:11 samn Exp $ 

print "Loading stats.hoc..."

//based on code from:
//hoc template that allows sampling from a pareto power law distribution 
//specified with objref rd
//rd = new rdmpareto($1=avg,$2=shape,[$3=seed])
//then picking values with .pick , or assigning to a vec with assignv(vec)
begintemplate rdmpareto
public avg,shape,rd,seed,pick,repick,paretoc,pareto5,assignv,reset,pareto4,pareto3
double avg[1],shape[1],seed[1]
objref rd
proc init () {
  avg=$1 shape=$2
  if(numarg()>2)seed=$3 else seed=1234
  rd=new Random()
proc reset () {
func paretoc () { local scale,shape,U
  scale=$1 shape=$2 U = rd.uniform(0,1)
  return scale * (1.0/ U^(1/shape) )
func pareto5 () { local avg,shape
  avg=$1 shape=$2
  return paretoc( avg * (shape -1)/shape, shape)
func pareto4 () { local alpha,u
  u = 1 - rd.uniform(0,1)
  return $1 + 1 / u^(1/alpha)
func pareto3 () { local x,z,b,a
  b = avg // 1 //min value
  a = shape // 10
  x = rd.uniform(0,1)
  z = x^-1/a
  return 1 + b * z
func pick () {
  return pareto5(avg,shape)
func repick () {
  return pick()
func assignv () { local i localobj vi
  for i=0,vi.size-1 vi.x(i)=pick()
endtemplate rdmpareto

func skew () { local a,ret localobj v1
  return ret

func skewv () { localobj v1
  v1=new Vector(5)
  return v1.x(4)

//** test rsampsig
objref vIN0,vIN1,vhsout,myrdm,vrs,VA
R0SZ=30000//size of group 0
R1SZ=30000//size of group 1
RPRC=100 // # of trials (combinations)
RS0M=0 //mean of group 0
RS1M=0 //mean of group 1
RS0SD=1 //sdev of group 0
RS1SD=1 //sdev of group 1
proc rsi () {
  if(myrdm==nil) myrdm=new Random()  
  {myrdm.normal(RS0M,RS0SD) vIN0=new Vector(R0SZ) vIN0.setrand(myrdm)}  
  {myrdm.normal(RS1M,RS1SD) vIN1=new Vector(R1SZ) vIN1.setrand(myrdm)}
  vhsout=new Vector(vIN0.size+vIN1.size)
    vrs=new Vector(RPRC)
  } else {
    vrs=new Vector(combs_stats(R0SZ+R1SZ,mmax(R0SZ,R1SZ))*RPRC)
  VA=new Vector()  VA.copy(vIN0) VA.append(vIN1)
func hocmeasure () {
  return vhsout.mean
func compfunc () {
  if(verbose_stats>1) printf("$1=%g,$2=%g\n",$1,$2)
  return hretval_stats
func testrs () { local dd localobj str
  if(numarg()>0)dd=$1 else dd=1
  str=new String()
    sprint(str.s,"p(abs(m0-m1))>%g=%g, t=%g, e=%g",abs(vIN0.mean-vIN1.mean),pval,tval,abs(pval-tval)/tval)
    {ge() ers=0 clr=1 hist(g,VA) clr=2  hist(g,vIN0) clr=3  hist(g,vIN1) g.label(0,0.95,str.s)}
    sprint(str.s,"m0=%g, m1=%g, n0=%g, n1=%g, s0=%g, s1=%g",vIN0.mean,vIN1.mean,vIN0.size,vIN1.size,vIN0.stdev,vIN1.stdev)
    g.exec_menu("View = plot")
  printf("pval=%g, tval=%g, err=%g\n",pval,tval,abs(pval-tval)/tval)
  return pval

//* nhppvec(intensityvec,dt,maxt[,se])
// returns a Vector of spike times generated by a nonhomogenous poisson process
// described by intensity function intensityvec, with dt time-step, maxt max time
// and se the seed for random # generator
// this algorithm is called 'thinning'
obfunc nhppvec () { local i,dt,tt,maxt,maxi,se,tidx localobj tvec,ivec,rdm
  tvec=new Vector(100e3) tvec.resize(0)
  ivec=$o1 dt=$2 maxt=$3
  if(numarg()>3)se=$4 else se=1234
  rdm=new Random()
  while(tt<maxt) {
    tt = tt - 1.0/maxi * log(rdm.uniform(0,1))
    tidx = tt / dt
    if(tidx >= ivec.size) break
    if(rdm.uniform(0,1) <= ivec.x(tidx) / maxi) {
  return tvec

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

References and models cited by this paper

References and models that cite this paper

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