Reinforcement learning of targeted movement (Chadderdon et al. 2012)

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Accession:144538
"Sensorimotor control has traditionally been considered from a control theory perspective, without relation to neurobiology. In contrast, here we utilized a spiking-neuron model of motor cortex and trained it to perform a simple movement task, which consisted of rotating a single-joint “forearm” to a target. Learning was based on a reinforcement mechanism analogous to that of the dopamine system. This provided a global reward or punishment signal in response to decreasing or increasing distance from hand to target, respectively. Output was partially driven by Poisson motor babbling, creating stochastic movements that could then be shaped by learning. The virtual forearm consisted of a single segment rotated around an elbow joint, controlled by flexor and extensor muscles. ..."
Reference:
1 . Chadderdon GL, Neymotin SA, Kerr CC, Lytton WW (2012) Reinforcement learning of targeted movement in a spiking neuronal model of motor cortex PLoS ONE 2012 7(10):e47251
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 fast spiking (FS) interneuron; Neocortex spiking regular (RS) neuron; Neocortex spiking low threshold (LTS) neuron;
Channel(s):
Gap Junctions:
Receptor(s): GabaA; AMPA; NMDA;
Gene(s):
Transmitter(s): Dopamine; Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Simplified Models; Synaptic Plasticity; Long-term Synaptic Plasticity; Reinforcement Learning; Reward-modulated STDP;
Implementer(s): Neymotin, Sam [samn at neurosim.downstate.edu]; Chadderdon, George [gchadder3 at gmail.com];
Search NeuronDB for information about:  GabaA; AMPA; NMDA; Dopamine; Gaba; Glutamate;
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arm1d
README
drspk.mod *
infot.mod *
intf6_.mod *
intfsw.mod *
misc.mod *
nstim.mod *
stats.mod *
updown.mod *
vecst.mod *
arm.hoc
basestdp.hoc
col.hoc *
colors.hoc *
declist.hoc *
decmat.hoc *
decnqs.hoc *
decvec.hoc *
default.hoc *
drline.hoc *
filtutils.hoc *
geom.hoc
grvec.hoc *
hinton.hoc *
infot.hoc *
init.hoc
intfsw.hoc *
labels.hoc *
local.hoc *
misc.h *
mosinit.hoc
network.hoc
nload.hoc
nqs.hoc *
nqsnet.hoc *
nrnoc.hoc *
params.hoc
run.hoc
samutils.hoc *
sense.hoc *
setup.hoc *
sim.hoc
simctrl.hoc *
stats.hoc *
stim.hoc
syncode.hoc *
units.hoc *
xgetargs.hoc *
                            
// $Id: filtutils.hoc,v 1.11 2011/11/07 21:37:48 samn Exp $ 

print "Loading filtutils.hoc..."

//* mkgauss(vector,average,standard-dev)
proc mkgauss () { local i,x,sz,mu,sd localobj vin
  vin=$o1 sz=vin.size mu=$2 sd=$3
  for vtr(&x,vin,&i) vin.x(i) = exp( -(x-mu)^2 / (2*sd*sd) )
  vin.mul( 1 / (sd*sqrt(2*PI)) )
}

//* mktriangwin(vec,size - should be odd,[skip the wraparound])
proc mktriangwin () { local i,j,sz localobj vin
  vin=$o1 vin.resize($2)
  vin.x(int($2/2))=1
  j=1 sz=1/(vin.size/2)
  for (i=int($2/2)-1;i>=0;i-=1) {
    vin.x(i)=j
    j-=sz
  }
  j=1
  for i=int($2/2)+1,vin.size-1 {
    vin.x(i)=j
    j-=sz
  }
  vin.div(vin.sum)
  if(numarg()>2) return
  vin.wraparound(vin.size)
}

//* mkgaussfilt(vec,stdev[,vx])
proc mkgaussfilt () { local sd,minx,maxx,dx,a localobj vin,vx
  vin=$o1 sd=$2
  if(numarg()>2) {vx=$o3 vin.resize(0) vin.copy(vx)}
  mkgauss(vin,0,sd)
  vin.wraparound(vin.size)
  vin.div(vin.sum)
  dealloc(a)
}

//* dofilt(vsignal,vwindow) - filters with convlv
proc dofilt () { local a,i localobj vsig,vwin,vtmp
  a=allocvecs(vtmp) vsig=$o1 vwin=$o2 sz=vsig.size
  vtmp.convlv(vsig,vwin)
  vsig.copy(vtmp)
  vsig.resize(sz) // make sure size doesn't change
  dealloc(a)  
}

//* triangfilt(vin,filtsize) - run a triangle filter
proc triangfilt () { local a localobj vin,vwin
  vin=$o1
  a=allocvecs(vwin)
  mktriangwin(vwin,$2) // make the window
  dofilt(vin,vwin) // do the filtering
  dealloc(a)
}

//* boxfilt(vin,filtsize) - run a box(moving average) filter
proc boxfilt () { local a localobj vin,vwin
  vin=$o1
  a=allocvecs(vwin)
  {vwin.resize($2) vwin.fill(1) vwin.div(vwin.size)} // make the window
  dofilt(vin,vwin) // do the filtering
  dealloc(a)
}

//* gaussfilt(vin,stdev,vx) - run a gaussian filter - vx is x-values used to make gaussian
proc gaussfilt () {  local a,sd localobj vin,vwin,vx
  vin=$o1 sd=$2 vx=$o3
  a=allocvecs(vwin)
  mkgaussfilt(vwin,sd,vx) // make the window
  dofilt(vin,vwin) // do the filtering
  dealloc(a)
}

//* myfilt(code,vec) - code:0=gauss,1=triangle,2=box
proc myfilt () { local a localobj vx
  if($1==0) {
    a=allocvecs(vx)
    vx.indgen(-3,3,.03)
    gaussfilt($o2,stdg,vx)
  } else if($1==1) {
    triangfilt($o2,winsz)
  } else if($1==2) {
    boxfilt($o2,winsz)
  }
}

//* resample(vec,new size) - resample a vec to new size using linear interpolation
proc resample(){ local newsz,idxdest,idxsrc,val,fctr,frac localobj vtmp
  {vtmp=new Vector($2) fctr=$o1.size/$2  vtmp.x(0)=$o1.x(0) idxsrc=fctr}
  for(idxdest=1;idxdest<$2-1;idxdest+=1){
    idxsrc = idxdest * fctr
    frac = idxsrc - int(idxsrc)
    idxsrc = int(idxsrc)
    if(idxsrc+1>=$o1.size){
      vtmp.x(idxdest) = $o1.x(idxsrc)
      continue
    }
    val = (1-frac) * $o1.x(idxsrc) + frac * $o1.x(idxsrc+1)
    vtmp.x(idxdest) = val
  }
  {vtmp.x($2-1)=$o1.x($o1.size-1) $o1.resize($2) $o1.copy(vtmp)}
}

//* bandfilt(datavector,low frequency,high frequency[,window size for bandpass filter])
// bandpass filter using via FFT convolution, $o1 will be modified
func bandfilt () { local a,idx,hz,lohz,hihz,wsz localobj v1,vwin,v2
  if(!name_declared("INSTALLED_myfft")){printf("bandfilt ERRA: myfft.mod not installed!\n") return 0}
  {v1=$o1  lohz=$2  hihz=$3}
  if(numarg()>3)wsz=$4 else wsz=1025
  a=allocvecs(vwin,v2)
  vwin.resize(wsz)
  vwin.bpwin(lohz/sampr,hihz/sampr) //make bandpass windowed sinc
  vwin.wraparound(wsz) //wrap around for FFT convolution
  v2.convlv(v1,vwin)
  v1.copy(v2)
  dealloc(a)
  return 1
}

//* multfilt(inputvector,outputvector,vector of lowfrequencies,vector of highfrequencies)
//get superposition of multiple frequency bands, each pair in $o3,$o4 should be considered a band
func multfilt () { local a,i localobj vtmp,vin,vout,vlo,vhi
  if(!name_declared("INSTALLED_myfft")) {printf("multfilt ERRA: myfft.mod not installed!\n") return 0}
  a=allocvecs(vtmp)
  vin=$o1 vout=$o2 vlo=$o3 vhi=$o4
  for i=0,vlo.size-1 {
    vtmp.copy(vin)
    bandfilt(vtmp,vlo.x(i),vhi.x(i))
    if(i==0)vout.copy(vtmp) else vout.add(vtmp)
  }
  vout.resize(vin.size)
  dealloc(a)  
  return 1
}

//* nrnpsd(vector,samplingrate) - calculates PSD and returns as an NQS object
// uses NEURON spctrm function
obfunc nrnpsd () { local sampr localobj vec,nqp
  vec=$o1 sampr=$2
  nqp=new NQS("f","pow")
  nqp.v[1].spctrm(vec)
  nqp.v.indgen(0,sampr/2,(sampr/2)/nqp.v[1].size)
  nqp.v.resize(nqp.v[1].size)
  return nqp
}

//* mkprefftwin(outputvec,windowtype[,windowsize]) - make window for multiplying before running fft
proc mkprefftwin () { local pref,sz localobj vpre
  vpre=$o1 pref=$2
  if(numarg()>2) sz=$3 else sz=vpre.size
  vpre.resize(sz)
  if(pref==1) {
    vpre.blackmanwin()
  } else if(pref==2) {
    vpre.hanningwin()
  } else if(pref==3) {
    vpre.hammingwin()
  } else {printf("mkprefftwin ERRA: unknown window type!\n") return}
}

//* getspecnq(vec,sampr[,specty,prefilt])
obfunc getspecnq () { local sampr,specty,pref,a localobj vec,vpre,v2,nq
  vec=$o1 sampr=$2 nq=nil
  if(numarg()>2) specty=$3 else specty=0
  if(numarg()>3) pref=$4 else pref=0
  if(pref) { // pre-fft filtering
    a=allocvecs(vpre,v2)
    vpre.resize(vec.size)
    mkprefftwin(vpre,pref)
    v2.copy(vec)
    v2.mul(vpre)
    vec=v2
  }
  if(specty==0) nq=pypmtm(vec,sampr)
  if(specty==1) nq=pypsd(vec,sampr)
  if(specty==2) nq=matpmtm(vec,sampr)
  if(specty==3) nq=nrnpsd(vec,sampr)
  if(specty==4) nq=pybspow(vec,sampr)
  if(pref) dealloc(a)
  return nq
}


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