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This notebook presents a simulation of 5000 ms of 400 descending commands and 800 motoneurons from soleus. The force is prduced by a Hill-type muscle model.



In [14]:

    
import sys
sys.path.insert(0, '..')
import time
import matplotlib.pyplot as plt
%matplotlib notebook
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 MotorUnitPool import MotorUnitPool
from NeuralTract import NeuralTract
from SynapsesFactory import SynapsesFactory
from jointAnkleForceTask import jointAnkleForceTask



In [15]:

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



In [16]:

    
pools = dict()
pools[0] = MotorUnitPool(conf, 'SOL')
pools[1] = NeuralTract(conf, 'CMExt')
ankle = jointAnkleForceTask(conf, pools)
Syn = SynapsesFactory(conf, pools)
del Syn









    



[ 2860.02638298]
Hill muscle of the SOL muscle with maximum force of 3586.0 N  built.
Muscle spindle from muscle SOL built.
Motor Unit Pool SOL built
Descending Command CMExt built
Ankle joint for Force Task built
All the 95810 synapses were built
All the 0 synaptic noises were built



In [17]:

    
t = np.arange(0.0, conf.simDuration_ms, conf.timeStep_ms)
conf.simDuration_ms = 5000 # Here I change simulation duration without changing the Configuration file.
GammaOrder = 10
FR = 1000/12.0



In [18]:

    
tic = time.time()
for i in xrange(0,len(t)-1): 
    ankle.atualizeAnkle(t[i], 0.1 * np.sin(2*np.pi*t[i]/1000.0))
    pools[1].atualizePool(t[i], FR, GammaOrder)
    pools[0].atualizeMotorUnitPool(t[i])
toc = time.time()
print str(toc - tic) + ' seconds'









    



8707.2010231 seconds



In [19]:

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

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



In [20]:

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









    














    











    Out[20]:





<matplotlib.text.Text at 0x7f25ac98dc90>

The muscle force produced by the Hill-type model is shown in Fig.\ref{fig:forceHill}.



In [21]:

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









    














    











    Out[21]:





<matplotlib.text.Text at 0x7f25a3c20490>

The muscle length computed with the Hill-type model is shown in Fig.\ref{fig:lengthHill}.



In [22]:

    
plt.figure()
plt.plot(t, pools[0].Muscle.length_m, '-')
plt.xlabel('t (ms)')
plt.ylabel('Muscle Length (m)')









    














    











    Out[22]:





<matplotlib.text.Text at 0x7f25a3baad50>

The muscle velocity, computed by the Hill-type muscle model, is in Fig.\ref{fig:velocityHill}.



In [23]:

    
plt.figure()
plt.plot(t, pools[0].Muscle.velocity_m_ms, '-')
plt.xlabel('t (ms)')
plt.ylabel('Muscle Velocity (m/ms)')









    














    











    Out[23]:





<matplotlib.text.Text at 0x7f25a3b3c510>

The ankle joint angle is shown in Fig. \ref{fig:ankleAngleHill}.



In [24]:

    
plt.figure()
plt.plot(t, ankle.ankleAngle_rad*180.0/np.pi, '-')
plt.xlabel('t (ms)')
plt.ylabel('Ankle angle (degree)')









    














    











    Out[24]:





<matplotlib.text.Text at 0x7f25a37d5c50>



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