Sensorimotor cortex reinforcement learning of 2-joint virtual arm reaching (Neymotin et al. 2013)

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Accession:150245
"... We developed a model of sensory and motor neocortex consisting of 704 spiking model-neurons. Sensory and motor populations included excitatory cells and two types of interneurons. Neurons were interconnected with AMPA/NMDA, and GABAA synapses. We trained our model using spike-timing-dependent reinforcement learning to control a 2-joint virtual arm to reach to a fixed target. ... "
Reference:
1 . 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 Neural Computation 25(12):3263-93 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Neocortex V1 pyramidal corticothalamic L6 cell; Neocortex U1 pyramidal intratelencephalic L2-5 cell; Neocortex V1 interneuron basket PV cell; 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): Gaba; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Synaptic Plasticity; Learning; Reinforcement Learning; STDP; Reward-modulated STDP; Sensory processing;
Implementer(s): Neymotin, Sam [samn at neurosim.downstate.edu]; Chadderdon, George [gchadder3 at gmail.com];
Search NeuronDB for information about:  Neocortex V1 pyramidal corticothalamic L6 cell; Neocortex V1 interneuron basket PV cell; Neocortex U1 pyramidal intratelencephalic L2-5 cell; GabaA; AMPA; NMDA; Gaba; Glutamate;
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a2dmodeldb
readme.html
drspk.mod *
infot.mod *
intf6_.mod *
misc.mod *
nstim.mod *
stats.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
labels.hoc *
misc.h *
mosinit.hoc
network.hoc
nload.hoc
nqs.hoc *
nqsnet.hoc *
nrnoc.hoc *
params.hoc
python.hoc
pywrap.hoc *
run.hoc
samutils.hoc *
screenshot.png
sense.hoc *
setup.hoc *
simctrl.hoc *
stats.hoc *
stim.hoc
syncode.hoc *
trainedplast.nqs
units.hoc *
xgetargs.hoc *
                            
// $Id: simctrl.hoc,v 1.14 2000/11/27 21:59:33 billl Exp $
// Graphic routines for neuremacs simulation control

proc sim_panel () {
  xpanel(simname)
        xvarlabel(output_file)
	xbutton("Init", "stdinit()")
	xbutton("Init & Run", "run()")
	xbutton("Stop", "stoprun=1")
	xbutton("Continue till Tstop", "continueRun(tstop)")
	xvalue("Continue till", "runStopAt", 1, "{continueRun(runStopAt) stoprun=1}", 1, 1)
	xvalue("Continue for", "runStopIn", 1, "{continueRun(t + runStopIn) stoprun=1}", 1,1)
	xbutton("Single Step", "steprun()")
	xvalue("Tstop", "tstop", 1, "tstop_changed()", 0, 1)
	graphmenu()
	sim_menu_bar()
	misc_menu_bar()
  xpanel()
}

proc misc_menu_bar() {
  xmenu("Miscellaneous")
    xbutton("Label Graphs", "labelgrs()")
    xbutton("Label With String", "labelwith()")
    xbutton("Label Panel", "labelpanel()")
	xbutton("Parameterized Function", "load_template(\"FunctionFitter\") makefitter()")
  xmenu()
}

proc sim_menu_bar() {
  xmenu("Simulation Control")
    xbutton("File Vers", "elisp(\"sim-current-files\")")
    xbutton("File Status...", "elisp(\"sim-rcs-status\")")
    xbutton("Sim Status", "elisp(\"sim-portrait\")")
    xbutton("Load Current Files", "elisp(\"sim-load-sim\")")
    xbutton("Load Templates", "elisp(\"sim-load-templates\")") 
    xbutton("Load File...", "elisp(\"sim-load-file\")") 
    xbutton("Save Sim...", "elisp(\"sim-save-sim\")")
    xbutton("Set File Vers...", "elisp(\"sim-set-file-ver\")")
    xbutton("Read Current Vers From Index", "elisp(\"sim-read-index-file\")")
    xbutton("Read Last Saved Vers", "elisp(\"sim-read-recent-versions\")")
    xbutton("Output to sim buffer", "elisp(\"sim-direct-output\")")
  xmenu()
}

proc labelpanel() {
  xpanel(simname,1)
	xvarlabel(output_file)
  xpanel()
}

proc labels () {
  labelwith($s1)
  labelgrs()
}

proc labelgrs () { local i, j, cnt
  for j=0,n_graph_lists-1 {
    cnt = graphList[j].count() - 1
    for i=0,cnt labelgr(graphList[j].object(i))
  }
}

proc labelwith () { local i, j, cnt
  temp_string_ = user_string_  // save the old one
  if (numarg() == 1) { /* interactive mode */  
    user_string_ = $s1
  } else {
    string_dialog("write what?", user_string_)
  }
  for j=0,n_graph_lists-1 {
    cnt = graphList[j].count() - 1
    for i=0,cnt {
      graphList[j].object(i).color(0)
      graphList[j].object(i).label(0.5,0.9,temp_string_)
      graphList[j].object(i).color(1)
      graphList[j].object(i).label(0.5,0.9,user_string_)
    }
  }
}

proc labelgr () { local i
  $o1.color(0)  // white overwrite
  for (i=0;i<10;i=i+1) { // erase every possible runnum for this date
    sprint(temp_string_,"%s %d%d",datestr,i,i)
    $o1.label(0.1,0.7,temp_string_) }
  $o1.color(1) // back to basic black
  sprint(temp_string_,"%s %02d",datestr,runnum)
  $o1.label(0.1,0.7,temp_string_)
}


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