This notebook presents a simulation of a pool of Renshaw cells (RC) with a current injected (iInjected) in their soma.

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import sys
sys.path.insert(0, '..')
import time
import matplotlib.pyplot as plt
%matplotlib inline  
import numpy as np

from Configuration import Configuration
from InterneuronPool import InterneuronPool
from SynapsesFactory import SynapsesFactory


    

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conf = Configuration('confInjectedCurrentRenshawCellPool.rmto')
conf.simDuration_ms = 100 # 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')


    

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pools = dict()
pools[0] = InterneuronPool(conf, 'RC', 'ext')

Syn = SynapsesFactory(conf, pools)


    

    




Interneuron Pool of RC ext built
All the 0 synapses were built
Synaptic Noise on RC_ext built
All the 350 synaptic noises were built

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# The simulation  itself

tic = time.time()
for i in xrange(0,len(t)-1):
    # Here you can change the injected current in the Resnhaw cell during the simulation
    for j in xrange(len(pools[0].unit)):
        pools[0].iInjected[j] = 5
    pools[1].atualizePool(t[i])    
    pools[0].atualizeInterneuronPool(t[i])
    membPotential[i] = pools[0].unit[175].v_mV # This command records the membrane potential of the RC with index 175.
toc = time.time()
print str(toc - tic) + ' seconds'


    

    




25.8434419632 seconds

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pools[0].listSpikes()

plt.plot(pools[0].poolSomaSpikes[:, 0],
    pools[0].poolSomaSpikes[:, 1]+1, '.')

plt.xlabel('t (ms)')
plt.ylabel('RC index')

plt.show()


    

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pools[1].listSpikes()

plt.plot(pools[1].poolTerminalSpikes[:, 0],
    pools[1].poolTerminalSpikes[:, 1]+1, '.')

plt.xlabel('t (ms)')
plt.ylabel('RC noise index')

plt.show()


    

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plt.figure()

plt.plot(t, membPotential, '-')

plt.xlabel('t (ms)')
plt.ylabel('Soma potential (mV)')

plt.show()


    

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