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: params.hoc,v 1.224 2012/05/04 03:43:33 samn Exp $

//* INTF6 params
mg_INTF6=1.6
EAM_INTF6=65 // these are deviations above RMP
ENM_INTF6=90
EGA_INTF6=-15

jrsvn_INTF6=1e3
jrsvd_INTF6=2e4
jrtm_INTF6 = 5e3

v_init=1000 // so keep v's as set randomly

mytstop= 300* 1e3  // used to create the stim noise

if(!autotune) if(useSTDP) seadsetting_INTF6 = 3 // plasticity on

//* Declarations

declare("fih","o[1]","nstim","o[1]")
vseed_stats(223481)
rdm.MCellRan4(seed_stats)

//* IREparams - setup IRE cells
proc IREparams () { local ii,jj localobj xo,co
  for ltr(co,lcol) for case(&ii,IRE) if(co.numc[ii]>0) {
    for jj=co.ix[ii],co.ixe[ii] { xo=co.ce.o(jj)
      
      xo.ahpwt=1
      xo.tauahp=400
      xo.RMP= -65
      xo.VTH= -40 
      xo.refrac= 1
      xo.Vblock= 50
      
      xo.tauGA  = 10
      xo.tauGA2 = 20
      xo.tauAM2 = 20
      xo.tauNM2 = 300
      
      xo.tauRR = 0.1
      xo.RRWght = .25 
    }
  }
}

//* TCparams - setup TC cells
proc TCparams () { local ii,jj localobj xo,co
  for ltr(co,lcol) for case(&ii,TC) if(co.numc[ii]>0) {
    for jj=co.ix[ii],co.ixe[ii] { xo=co.ce.o(jj)
      
      xo.ahpwt=1
      xo.tauahp=400
      xo.RMP= -65
      xo.VTH= -40 
      xo.refrac= 1
      xo.Vblock= 50
      
      xo.tauGA  = 10
      xo.tauGA2 = 20
      xo.tauAM2 = 20
      xo.tauNM2 = 300
      
      xo.tauRR = 0.1 
      xo.RRWght = .25 
    }
  }
}

//* RSparams - setup regular spiking excitatory cells
proc RSparams () { local ii,jj localobj xo,co
  for ltr(co,lcol) for case(&ii,ES,EM,E2,E4,E5R,E5B,E6) if(co.numc[ii]>0) {
    for jj=co.ix[ii],co.ixe[ii] { xo=co.ce.o(jj)
      
      xo.ahpwt=1
      xo.tauahp=400
      xo.RMP= -65
      xo.VTH= -40 
      xo.refrac=  5
      xo.Vblock= -25
      
      xo.tauGA  = 10
      xo.tauGA2 = 20
      xo.tauAM2 = 20
      xo.tauNM2 = 300
      
      xo.tauRR = 8 
      xo.RRWght = .75 
    }
  }
}

//* LTSparams - setup low-threshold spiking interneurons
proc LTSparams () { local ii,jj localobj xo,co
  for ltr(co,lcol) for case(&ii,ISL,IML,I2L,I4L,I5L,I6L) if(co.numc[ii]>0) {
    for jj=co.ix[ii],co.ixe[ii] { xo=co.ce.o(jj)  
      xo.ahpwt=0.5
      xo.refrac= 2.5
      xo.tauahp=50
      xo.Vblock=-10    
      xo.RMP = -65
      xo.VTH= -47

      xo.tauGA  = 10
      xo.tauGA2 = 20
      xo.tauAM2 = 20
      xo.tauNM2 = 300
      
      xo.tauRR = 1.5
      xo.RRWght = 0.25
    }
  }
}

//* FSparams - setup fast spiking interneurons
proc FSparams () { local ii,jj localobj xo,co
  for ltr(co,lcol) for case(&ii,IS,IM,I2,I2C,I4,I5,I6,I6C) if(co.numc[ii]>0) {
    for jj=co.ix[ii],co.ixe[ii] { xo=co.ce.o(jj)    
      xo.ahpwt=0.5
      xo.refrac= 2.5
      xo.tauahp=50
      xo.Vblock=-10    
      xo.RMP = -63
      xo.VTH= -40

      xo.tauGA  = 10
      xo.tauGA2 = 20
      xo.tauAM2 = 20
      xo.tauNM2 = 300
      
      xo.tauRR = 1.5
      xo.RRWght = 0.25
    }
  }
}

//* function calls

// cell params
IREparams()
TCparams()
RSparams()
LTSparams()
FSparams()


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