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 * *
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comppowspec.hoc *
condisconcellfig.hoc *
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// $Id: nqsnet.hoc,v 1.65 2010/09/07 18:56:17 samn Exp $
// xopen("nqsnet.hoc")

//      pre-id  post-id  pre#  post#   distance weight  syn-id   nc ptr  wt1 (eg AMPA+NMDA)
objref nq[2],sq[CTYPi][CTYPi],cp
obfunc mkcp0 () { localobj lo
  lo = new NQS("PRID","POID","STYP","PIJ","DIV","CONV","NSYN","NPRE")
  lo.useslist("PRID",CTYP) lo.useslist("POID",CTYP) lo.useslist("STYP",STYP)
  return lo

obfunc mksp () { localobj lo
  lo=new NQS("CODE","PR","PO","DEL","WT0","WT1") // CODE==PRID(1),POID(2),COLA(3),COLB(4)
  // lo.useslist("PRID",CTYP) lo.useslist("POID",CTYP) 
  return lo

//* Numbers and connectivity params

// layer return layer location with 'sublayer' defined by Inhib (+0.5) or other suffix
// E or I should be 1st letter of name, suffix letter will ideally dichotomize into late
// alphabet or early alphabet
func layer () { local x,in,la
  if (sscanf(CTYP.o($1).s,"%c%d%c",&in,&x,&la)<2) return -1
  if (x==23) x=3 // layer 2/3
  if (in==73) x+=0.5 // ascii 73 is 'I'
  if (la>77) x+=0.2 // <='M'
  return x

//* routines
//** styp() sets synapse type based on presynaptic cell
func styp () { local pr,po
  pr=$1 po=$2
  if (pr==IN && po==IN) { return GA 
  } else if (pr==IN) { return IX
  } else if (pr==SU || pr==DP) { return EX
  } else if (pr==SM) { return AM
  } else if (strm(CTYP.o[pr].s,"^E")) { return EX
  } else if (strm(CTYP.o[pr].s,"^I")) { return IX
  } else printf("styp ERR %s->%s not classified",CTYP.object(pr).s,CTYP.object(po).s)

//** ellfld() place the cells inside an ellipse
// r for an ellipse = a*b/sqrt((a*sin(theta))^2 + (b*cos(theta))^2)
proc ellfld () { local a,b,ii,jj,p,seed localobj xv,yv,xo
  a=1 b=2
  p=allocvecs(xv,yv) vrsz(allcells*10,xv,yv)
  xv.setrnd(4,2*a,seed) yv.setrnd(4,2*b) xv.sub(a) yv.sub(b)
  for vtr2(&x,&y,xv,yv,&ii) {
    if (a*x^2+b*y^2<1) { ce.o(jj).xloc=x ce.o(jj).yloc=y jj+=1 }
    if (jj==ce.count) break
  print ii,jj
  if (jj!=ce.count) print "Not filled"

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