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 10:28 [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 M1 L5B pyramidal pyramidal tract GLU cell; Neocortex M1 L2/6 pyramidal intratelencephalic GLU cell; Neocortex M1 interneuron basket PV GABA 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; Touch;
Implementer(s): Neymotin, Sam [samn at neurosim.downstate.edu]; Dura, Salvador [ salvadordura at gmail.com];
Search NeuronDB for information about:  Neocortex M1 L2/6 pyramidal intratelencephalic GLU cell; Neocortex M1 L5B pyramidal pyramidal tract GLU cell; Neocortex M1 interneuron basket PV GABA 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 *
                            
"""
BICOLORMAP

This program generators a two-color map, blue for negative, red for
positive changes, with grey in the middle. The input argument is how much
of a color gap there is between the red scale and the blue one.

The function has four parameters:
    gap: sets how big of a gap between red and blue color scales there is (0=no gap; 1=pure red and pure blue)
    mingreen: how much green to include at the extremes of the red-blue color scale
    redbluemix: how much red to mix with the blue and vice versa at the extremes of the scale
    epsilon: what fraction of the colormap to make gray in the middle

Examples:
    bicolormap(gap=0,mingreen=0,redbluemix=1,epsilon=0) # From pure red to pure blue with white in the middle
    bicolormap(gap=0,mingreen=0,redbluemix=0,epsilon=0.1) # Red -> yellow -> gray -> turquoise -> blue
    bicolormap(gap=0.3,mingreen=0.2,redbluemix=0,epsilon=0.01) # Red and blue with a sharp distinction between

Version: 2013sep13 by cliffk
"""

## Create colormap
def bicolormap(gap=0.1,mingreen=0.2,redbluemix=0.5,epsilon=0.01):
   from matplotlib.colors import LinearSegmentedColormap as makecolormap
   
   mng=mingreen; # Minimum amount of green to add into the colors
   mix=redbluemix; # How much red to mix with the blue an vice versa
   eps=epsilon; # How much of the center of the colormap to make gray
   omg=1-gap # omg = one minus gap
   
   cdict = {'red': ((0.00000, 0.0, 0.0),
                    (0.5-eps, mix, omg),
                    (0.50000, omg, omg),
                    (0.5+eps, omg, 1.0),
                    (1.00000, 1.0, 1.0)),

         'green':  ((0.00000, mng, mng),
                    (0.5-eps, omg, omg),
                    (0.50000, omg, omg),
                    (0.5+eps, omg, omg),
                    (1.00000, mng, mng)),

         'blue':   ((0.00000, 1.0, 1.0),
                    (0.5-eps, 1.0, omg),
                    (0.50000, omg, omg),
                    (0.5+eps, omg, mix),
                    (1.00000, 0.0, 0.0))}
   cmap = makecolormap('bicolormap',cdict,256)

   return cmap

## Show the staggering beauty of the color map's infinite possibilities
def testcolormap():
    from pylab import figure, subplot, imshow, colorbar, rand, show
    
    maps=[]
    maps.append(bicolormap()) # Default ,should work for most things
    maps.append(bicolormap(gap=0,mingreen=0,redbluemix=1,epsilon=0)) # From pure red to pure blue with white in the middle
    maps.append(bicolormap(gap=0,mingreen=0,redbluemix=0,epsilon=0.1)) # Red -> yellow -> gray -> turquoise -> blue
    maps.append(bicolormap(gap=0.3,mingreen=0.2,redbluemix=0,epsilon=0.01)) # Red and blue with a sharp distinction between
    nexamples=len(maps)
    
    figure(figsize=(5*nexamples,4))    
    for m in range(nexamples):
        subplot(1,nexamples,m+1)
        imshow(rand(20,20),cmap=maps[m],interpolation='none');
        colorbar()
    show()

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