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 7:e47251 [PubMed]
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;
/
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: params.hoc,v 1.223 2012/02/29 18:59:19 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 = 1e3

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

refrac_DRSPK = 10 // slightly longer refrac

// sim runs for:
//  BaseDur s of baseline (A)
//  turn on zip
//  ZipDur s              (B)
//  turn on plasticity
//  LearnDur s            (C)
//  turn off plasticity
//  BaseDur s             (D)

declare("LearnDur",0)
declare("BaseDur",100)
declare("ZipDur",0)

tstop=mytstop=htmax= (LearnDur + ZipDur + BaseDur*2) * 1e3

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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