Synchronization of integrate-and-fire oscillators

V. Hunter Adams (

In [2]:
import numpy
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = [21, 7.77]
from IPython.core.display import HTML
from matplotlib import animation, rc
from IPython.display import HTML
from IPython.display import clear_output
from matplotlib.pyplot import figure
import matplotlib
matplotlib.rcParams['animation.embed_limit'] = 2**128
.output_png {
    display: table-cell;
    text-align: center;
    vertical-align: middle;

function code_toggle() {
 if (code_show){
 } else {
 code_show = !code_show
$( document ).ready(code_toggle);
<form action="javascript:code_toggle()"><input type="submit" value="Click here to toggle on/off the raw code."></form>''')

Integrate-and-fire oscillators

An integrate-and-fire oscillator is a system that integrates a function until some threshold value is reached, at which point the system "fires" and the integrator is reset to zero. An animation of such a system is shown below. These systems have been used to model neurons, pacemaker cells in hearts, and other biological systems that synchronize [1].

In [3]:
x = numpy.arange(0, 100, 1)

red_position  = 0

# First set up the figure, the axis, and the plot element we want to animate
fig, ax = plt.subplots(figsize=(21, 7.77))

ax.set_xlim((0, 100))
ax.set_ylim((-1, 11))
ann = ax.annotate("firing threshold", xy=(1, 10.1), xytext=(1, 10.1), fontsize=18)
ax.set_title('An integrate-and-fire oscillator', fontsize=28)
ax.set_xlabel('time', fontsize=18)
ax.set_ylabel('$\sqrt{time}$', fontsize=18)

line,  = ax.plot(x, numpy.sqrt(x))
line1, = ax.plot([],[], 'r.', alpha=0.7, markersize=30)
line2, = ax.plot(x, numpy.ones(len(x))*10, 'b--', linewidth=0.5)

# initialization function: plot the background of each frame
def init():
    line.set_data(x, numpy.sqrt(x))
    line1.set_data(x[red_position], [numpy.sqrt(x[red_position])])
    line2.set_data(x, numpy.ones(len(x))*10)
    return (line, line1, line2, )

def animate(i):
    line.set_data(x, numpy.sqrt(x))
    line2.set_data(x, numpy.ones(len(x))*10)
    global red_position
    red_position = (red_position + 1)%100
    line1.set_data([x[red_position]], [numpy.sqrt(x[red_position])])
    return (line, line1, line2, )

# call the animator. blit=True means only re-draw the parts that have changed.
anim = animation.FuncAnimation(fig, animate, init_func=init,
                               frames=100, interval=20, blit=True)
rc('animation', html='jshtml')

In [4]: