1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
|
#!/usr/bin/env python
"""
Jeffress model, adapted with spiking neuron models.
A sound source (white noise) is moving around the head.
Delay differences between the two ears are used to determine the azimuth of the
source. Delays are mapped to a neural place code using delay lines (each neuron
receives input from both ears, with different delays).
"""
from brian2 import *
defaultclock.dt = .02*ms
# Sound
sound = TimedArray(10 * randn(50000), dt=defaultclock.dt) # white noise
# Ears and sound motion around the head (constant angular speed)
sound_speed = 300*metre/second
interaural_distance = 20*cm # big head!
max_delay = interaural_distance / sound_speed
print("Maximum interaural delay: %s" % max_delay)
angular_speed = 2 * pi / second # 1 turn/second
tau_ear = 1*ms
sigma_ear = .1
eqs_ears = '''
dx/dt = (sound(t-delay)-x)/tau_ear+sigma_ear*(2./tau_ear)**.5*xi : 1 (unless refractory)
delay = distance*sin(theta) : second
distance : second # distance to the centre of the head in time units
dtheta/dt = angular_speed : radian
'''
ears = NeuronGroup(2, eqs_ears, threshold='x>1', reset='x = 0',
refractory=2.5*ms, name='ears', method='euler')
ears.distance = [-.5 * max_delay, .5 * max_delay]
traces = StateMonitor(ears, 'delay', record=True)
# Coincidence detectors
num_neurons = 30
tau = 1*ms
sigma = .1
eqs_neurons = '''
dv/dt = -v / tau + sigma * (2 / tau)**.5 * xi : 1
'''
neurons = NeuronGroup(num_neurons, eqs_neurons, threshold='v>1',
reset='v = 0', name='neurons', method='euler')
synapses = Synapses(ears, neurons, on_pre='v += .5')
synapses.connect()
synapses.delay['i==0'] = '(1.0*j)/(num_neurons-1)*1.1*max_delay'
synapses.delay['i==1'] = '(1.0*(num_neurons-j-1))/(num_neurons-1)*1.1*max_delay'
spikes = SpikeMonitor(neurons)
run(1000*ms)
# Plot the results
i, t = spikes.it
subplot(2, 1, 1)
plot(t/ms, i, '.')
xlabel('Time (ms)')
ylabel('Neuron index')
xlim(0, 1000)
subplot(2, 1, 2)
plot(traces.t/ms, traces.delay.T/ms)
xlabel('Time (ms)')
ylabel('Input delay (ms)')
xlim(0, 1000)
tight_layout()
show()
|
