Cortical model with reinforcement learning drives realistic virtual arm (Dura-Bernal et al 2015)

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Accession:183014
We developed a 3-layer sensorimotor cortical network of consisting of 704 spiking model-neurons, including excitatory, fast-spiking and low-threshold spiking interneurons. Neurons were interconnected with AMPA/NMDA, and GABAA synapses. We trained our model using spike-timing-dependent reinforcement learning to control a virtual musculoskeletal human arm, with realistic anatomical and biomechanical properties, to reach a target. Virtual arm position was used to simultaneously control a robot arm via a network interface.
References:
1 . Dura-Bernal S, Zhou X, Neymotin SA, Przekwas A, Francis JT, Lytton WW (2015) Cortical Spiking Network Interfaced with Virtual Musculoskeletal Arm and Robotic Arm. Front Neurorobot 9:13 [PubMed]
2 . Dura-Bernal S, Li K, Neymotin SA, Francis JT, Principe JC, Lytton WW (2016) Restoring behavior via inverse neurocontroller in a lesioned cortical spiking model driving a virtual arm. Front. Neurosci. Neuroprosthetics 10:28
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 M1 pyramidal pyramidal tract L5B cell; Neocortex M1 pyramidal intratelencephalic L2-5 cell; Neocortex M1 interneuron basket PV cell; Neocortex fast spiking (FS) interneuron; Neostriatum fast spiking 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; Python (web link to model);
Model Concept(s): Synaptic Plasticity; Learning; Reinforcement Learning; STDP; Reward-modulated STDP; Sensory processing; Motor control;
Implementer(s): Neymotin, Sam [samn at neurosim.downstate.edu]; Dura, Salvador [ salvadordura at gmail.com];
Search NeuronDB for information about:  Neocortex M1 pyramidal intratelencephalic L2-5 cell; Neocortex M1 pyramidal pyramidal tract L5B cell; Neocortex M1 interneuron basket PV cell; GabaA; AMPA; NMDA; Gaba; Glutamate;
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arm2dms_modeldb
mod
msarm
stimdata
README.html
analyse_funcs.py
analysis.py
armGraphs.py
arminterface_pipe.py
basestdp.hoc
bicolormap.py
boxes.hoc *
bpf.h *
col.hoc
colors.hoc *
declist.hoc *
decmat.hoc *
decnqs.hoc *
decvec.hoc *
default.hoc *
drline.hoc *
filtutils.hoc *
grvec.hoc
hinton.hoc *
hocinterface.py
infot.hoc *
init.hoc
intfsw.hoc *
labels.hoc
load.hoc
load.py
local.hoc *
main.hoc
main_demo.hoc
main_neurostim.hoc
misc.h *
misc.py *
msarm.hoc
network.hoc
neuroplot.py *
neurostim.hoc
nload.hoc
nqs.hoc *
nqsnet.hoc *
nrnoc.hoc
params.hoc
perturb.hoc
python.hoc
pywrap.hoc *
run.hoc
runbatch_neurostim.py
runsim_neurostim
samutils.hoc *
saveoutput.hoc
saveoutput2.hoc
setup.hoc *
sim.hoc
sim.py
sim_demo.py
simctrl.hoc *
stats.hoc *
stim.hoc
syncode.hoc *
units.hoc *
vector.py
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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