mosinit.hoc

//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
//
// This simulation was used in an article in press at Neural Computation
//  Citation: Neymotin SA, Chadderdon GL, Kerr CC, Francis JT, Lytton WW (2013).
//  Reinforcement learning of 2-joint  virtual arm reaching in a computer model of sensorimotor cortex.
//
// For questions/comments on the simulation contact Sam Neymotin (email: samn at neurosim dot downstate dot edu)
//
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////

xopen("./setup.hoc")
xopen("./nrnoc.hoc")
load_file("init.hoc")
load_file("nqsnet.hoc")
load_file("network.hoc")
load_file("params.hoc")
load_file("stim.hoc")
load_file("sense.hoc")
load_file("run.hoc")
load_file("nload.hoc")
load_file("basestdp.hoc")
load_file("arm.hoc")

objref tnq
tnq = new NQS("trainedplast.nqs") // stored weights from training
tstop = 15e3 // 15 second reach

//* settrained - set weights to learned values
proc settrained () { localobj nq
  resetplast_INTF6=0
  setplastnq(tnq,col[0]) // this loads the learned weights 
  print "loaded trained weights."
}

//* setnaive - reset the weights to values prior to training
proc setnaive () {
  resetplast_INTF6=1
  print "set naive weights"
}

//* drawTrajTarg - draw trajectory from run + start + target
proc drawTrajTarg () { local xt,yt
  g=Graph[0]
  g.erase()
  xt=tPos.x yt=tPos.y
  {rotArmTo(sAng[0],sAng[1]) tPos.x=armPos.x tPos.y=armPos.y drtarg(9)}
  tPos.x=xt tPos.y=yt
  {gvt=gvmarkflag gvmarkflag=0 if(nqa==nil) return}
  {tPos.x=xt tPosy=yt}
  {rotArmTo(tAng[0],tAng[1]) tPos.x=armPos.x tPos.y=armPos.y drarm(0) drtarg(1)}
  nqa.gr("y","x",0,1,1)
  g.size(-3,3,-3,3)
}

//* run simulation and display output
proc mydemorun () {
  myrun()
  drawTrajTarg() // draw
  {drit(0,15,1) rasterlines(1)}// draw raster
  g[1].size(0,tstop,0,col.allcells+col.numc[DP]) // size the graph
}

//* run trained network, display output
proc trainedrun () {
  settrained()
  mydemorun()
}

//* run naive network, display output
proc naiverun () {
  setnaive() 
  mydemorun()
}

objref hb

//* make the gui
proc setgui () { local i
  {hb=new HBox() hb.intercept(1)}
  xpanel("Reinforcement learning of 2-joint virtual arm reaching in a computer model of sensorimotor cortex")
  xlabel("Simulation from: Reinforcement learning")
  xlabel("of 2-joint virtual arm reaching in a")
  xlabel("computer model of sensorimotor cortex")
  xlabel("by Neymotin et al.")
  xlabel("Neural Computation, 2013 (in press)")
  xlabel("For questions/comments contact Sam Neymotin")
  xlabel("email: samn at neurosim dot downstate dot edu")
  xlabel(" ")
  xbutton("Run trained network","trainedrun()")
  xbutton("Run naive network", "naiverun()")
  xbutton("Quit","quit()")
  xpanel()
  for i=0,1 g[i]=new Graph()
  {hb.intercept(0) hb.map()}
}
setgui()
tstop=15e3