87 lines
2.1 KiB
Python
87 lines
2.1 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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def hompoisson(rate, trials, duration) :
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spikes = []
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for k in range(trials) :
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times = []
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t = 0.0
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while t < duration :
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t += np.random.exponential(1/rate)
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times.append( t )
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spikes.append( times )
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return spikes
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def inhompoisson(rate, trials, dt) :
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spikes = []
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p = rate*dt
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for k in range(trials) :
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x = np.random.rand(len(rate))
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times = dt*np.nonzero(x<p)[0]
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spikes.append( times )
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return spikes
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def pifspikes(input, trials, dt, D=0.1) :
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vreset = 0.0
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vthresh = 1.0
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tau = 1.0
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spikes = []
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for k in range(trials) :
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times = []
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v = vreset
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noise = np.sqrt(2.0*D)*np.random.randn(len(input))/np.sqrt(dt)
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for k in range(len(noise)) :
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v += (input[k]+noise[k])*dt/tau
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if v >= vthresh :
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v = vreset
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times.append(k*dt)
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spikes.append( times )
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return spikes
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# parameter:
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rate = 20.0
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drate = 50.0
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trials = 10
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duration = 2.0
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dt = 0.001
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tau = 0.1;
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# homogeneous spike trains:
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homspikes = hompoisson(rate, trials, duration)
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# OU noise:
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rng = np.random.RandomState(54637281)
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time = np.arange(0.0, duration, dt)
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x = np.zeros(time.shape)+rate
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n = rng.randn(len(time))*drate*tau/np.sqrt(dt)+rate
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for k in range(1,len(x)) :
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x[k] = x[k-1] + (n[k]-x[k-1])*dt/tau
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x[x<0.0] = 0.0
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# inhomogeneous spike trains:
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#inhspikes = inhompoisson(x, trials, dt)
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# pif spike trains:
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inhspikes = pifspikes(x, trials, dt, D=0.3)
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fig = plt.figure( figsize=(9,4) )
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ax = fig.add_subplot(1, 2, 1)
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ax.set_title('stationary')
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ax.set_xlim(0.0, duration)
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ax.set_ylim(-0.5, trials-0.5)
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ax.set_xlabel('Time [s]')
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ax.set_ylabel('Trials')
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ax.eventplot(homspikes, colors=[[0, 0, 0]], linelength=0.8)
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ax = fig.add_subplot(1, 2, 2)
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ax.set_title('non-stationary')
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ax.set_xlim(0.0, duration)
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ax.set_ylim(-0.5, trials-0.5)
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ax.set_xlabel('Time [s]')
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ax.set_ylabel('Trials')
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ax.eventplot(inhspikes, colors=[[0, 0, 0]], linelength=0.8)
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plt.tight_layout()
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plt.savefig('rasterexamples.pdf')
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plt.close()
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