113 lines
3.4 KiB
Python
113 lines
3.4 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.ticker as mpt
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from plotstyle import *
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rate = 20.0
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trials = 20
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duration = 100.0
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dt = 0.001
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drate = 50.0
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tau = 0.1;
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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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def oupifspikes(rate, trials, duration, dt, D, drate, tau):
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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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spikes = pifspikes(x, trials, dt, D)
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return spikes
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def plot_count_fano(ax1, ax2, spikes):
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wins = np.logspace(-2, 0.0, 200)
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mean_counts = np.zeros(len(wins))
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var_counts = np.zeros(len(wins))
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for k, win in enumerate(wins):
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counts = []
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for times in spikes:
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c, _ = np.histogram(times, np.arange(0.0, duration, win))
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counts.extend(c)
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mean_counts[k] = np.mean(counts)
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var_counts[k] = np.var(counts)
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ax1.plot(mean_counts, var_counts, zorder=100, **lsA)
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ax1.set_xlabel('Mean count')
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ax1.set_xlim(0.0, 20.0)
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ax1.set_ylim(0.0, 20.0)
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ax1.set_xticks(np.arange(0.0, 21.0, 10.0))
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ax1.set_yticks(np.arange(0.0, 21.0, 10.0))
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ax2.plot(1000.0*wins, var_counts/mean_counts, **lsB)
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ax2.set_xlabel('Window', 'ms')
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ax2.set_ylim(0.0, 1.2)
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ax2.set_xscale('log')
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ax2.set_xticks([10, 100, 1000])
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ax2.set_xticklabels(['10', '100', '1000'])
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ax2.xaxis.set_minor_locator(mpt.NullLocator())
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ax2.set_yticks(np.arange(0.0, 1.2, 0.5))
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if __name__ == "__main__":
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homspikes = hompoisson(rate, trials, duration)
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inhspikes = oupifspikes(rate, trials, duration, dt, 0.3, drate, tau)
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fig, axs = plt.subplots(2, 2)
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fig.subplots_adjust(**adjust_fs(fig, top=0.5, right=2.0))
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plot_count_fano(axs[0,0], axs[0,1], homspikes)
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axs[0,0].text(0.1, 0.95, 'Poisson', transform=axs[0,0].transAxes)
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axs[0,0].set_xlabel('')
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axs[0,1].set_xlabel('')
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axs[0,0].xaxis.set_major_formatter(mpt.NullFormatter())
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axs[0,1].xaxis.set_major_formatter(mpt.NullFormatter())
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plot_count_fano(axs[1,0], axs[1,1], inhspikes)
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axs[1,1].axhline(1.0, **lsGrid)
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axs[1,0].text(0.1, 0.95, 'OU noise', transform=axs[1,0].transAxes)
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fig.text(0.01, 0.58, 'Count variance', va='center', rotation='vertical')
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fig.text(0.51, 0.58, 'Fano factor', va='center', rotation='vertical')
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plt.savefig('fanoexamples.pdf')
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plt.close()
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