This notebook is a simulation of 5000 ms of 400 independent descending commands following a gamma distribution with mean of 12 ms and order 10 and the Soleus muscle (800 motoneurons). Each descending command connects to approximately 30 % of the motor units. Also, a pool of 350 Renshaw cells is present.



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import sys
sys.path.insert(0, '..')
import time
import matplotlib.pyplot as plt
%matplotlib inline  
from IPython.display import set_matplotlib_formats
set_matplotlib_formats('pdf', 'png')
plt.rcParams['savefig.dpi'] = 75

plt.rcParams['figure.autolayout'] = False
plt.rcParams['figure.figsize'] = 10, 6
plt.rcParams['axes.labelsize'] = 18
plt.rcParams['axes.titlesize'] = 20
plt.rcParams['font.size'] = 16
plt.rcParams['lines.linewidth'] = 2.0
plt.rcParams['lines.markersize'] = 8
plt.rcParams['legend.fontsize'] = 14

plt.rcParams['text.usetex'] = True
plt.rcParams['font.family'] = "serif"
plt.rcParams['font.serif'] = "cm"
plt.rcParams['text.latex.preamble'] = "\usepackage{subdepth}, \usepackage{type1cm}"

import numpy as np

from Configuration import Configuration
from MotorUnitPoolNoChannel import MotorUnitPoolNoChannel
from InterneuronPoolNoChannel import InterneuronPoolNoChannel
from NeuralTract import NeuralTract
from SynapsesFactory import SynapsesFactory



In [2]:

    
conf = Configuration('confMNPoolWithRenshawCells.rmto')
conf.simDuration_ms = 5000 # Here I change simulation duration without changing the Configuration file.



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# Time vector for the simulation
t = np.arange(0.0, conf.simDuration_ms, conf.timeStep_ms)

membPotential = np.zeros_like(t, dtype = 'd')



In [4]:

    
pools = dict()
pools[0] = MotorUnitPoolNoChannel(conf, 'SOL')
pools[1] = NeuralTract(conf, 'CMExt')
pools[2] = InterneuronPoolNoChannel(conf, 'RC', 'ext')

Syn = SynapsesFactory(conf, pools)
GammaOrder = 10
FR = 1000/12.0









    



Muscle spindle from muscle SOL built.
Motor Unit Pool SOL built
Descending Command CMExt built
Interneuron Pool of RC ext built
All the 124404 synapses were built
Synaptic Noise on RC_ext built
All the 350 synaptic noises were built



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tic = time.time()
for i in xrange(0, len(t)-1):
    pools[1].atualizePool(t[i], FR, GammaOrder) # NeuralTract
    pools[0].atualizeMotorUnitPool(t[i]) # MN pool
    pools[3].atualizePool(t[i]) # RC synaptic Noise
    pools[2].atualizeInterneuronPool(t[i]) # RC pool
toc = time.time()
print str(toc - tic) + ' seconds'



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pools[0].listSpikes()
pools[1].listSpikes()
pools[2].listSpikes()

The spike times of all descending commands along the 5000 ms of simulation is shown in Fig. \ref{fig:spikesDescRenshaw}.



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plt.figure()
plt.plot(pools[1].poolTerminalSpikes[:, 0],
         pools[1].poolTerminalSpikes[:, 1]+1, '.')
plt.xlabel('t (ms)')
plt.ylabel('Descending Command index')

The spike times of the MNs along the 5000 ms of simulation is shown in Fig. \ref{fig:spikesMNRenshaw}.



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plt.figure()
plt.plot(pools[0].poolTerminalSpikes[:, 0],
         pools[0].poolTerminalSpikes[:, 1]+1, '.')
plt.xlabel('t (ms)')
plt.ylabel('Motor Unit index')

The spike times of the Renshaw cells along the 5000 ms of simulation is shown in Fig. \ref{fig:spikesRenshawRenshaw}.



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plt.figure()
plt.plot(pools[2].poolSomaSpikes[:, 0],
         pools[2].poolSomaSpikes[:, 1]+1, '.')
plt.xlabel('t (ms)')
plt.ylabel('Renshaw cell index')

The muscle force during the simulation \ref{fig:forceRenshaw}.



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plt.figure()
plt.plot(t, pools[0].Muscle.force, '-')
plt.xlabel('t (ms)')
plt.ylabel('Muscle force (N)')



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