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examples-twister_anonymous.txt
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examples-twister_anonymous.txt
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.. currentmodule:: brian
.. index::
pair: example usage; SpikeMonitor
.. _example-twister_anonymous:
Example: anonymous (twister)
============================
Anonymous entry for the 2012 Brian twister.
::
'''
My contribution to the brian twister!
I meant to give it more thought, but I forgot about the deadline!
'''
from brian import *
from brian.hears import *
import pygame
_mixer_status = [-1,-1]
class SoundMonitor(SpikeMonitor):
"""
Listen to you networks!
Plays pure tones whenever a neuron spikes, frequency is set according to the neuron number.
"""
def __init__(self, source, record=False, delay=0,
frange = (100.*Hz, 5000.*Hz),
duration = 50*ms,
samplerate = 44100*Hz):
super(SoundMonitor, self).__init__(source, record = record, delay = delay)
self.samplerate = samplerate
self.nsamples = np.rint(duration * samplerate)
p = linspace(0, 1, len(source)).reshape((1, len(source)))
p = np.tile(p, (self.nsamples, 1))
freqs = frange[0] * p + (1-p) * frange[1]
del p
times = linspace(0*ms, duration, self.nsamples).reshape((self.nsamples, 1))
times = np.tile(times, (1, len(source)))
self.sounds = np.sin(2 * np.pi * freqs * times)
self._init_mixer()
def propagate(self, spikes):
if len(spikes):
data = np.sum(self.sounds[:,spikes], axis = 1)
x = array((2 ** 15 - 1) * clip(data/amax(data), -1, 1), dtype=int16)
x.shape = x.size
# Make sure pygame receives an array in C-order
x = pygame.sndarray.make_sound(np.ascontiguousarray(x))
x.play()
def _init_mixer(self):
global _mixer_status
if _mixer_status==[-1,-1] or _mixer_status[0]!=1 or _mixer_status != self.samplerate:
pygame.mixer.quit()
pygame.mixer.init(int(self.samplerate), -16, 1)
_mixer_status=[1,self.samplerate]
def test_cuba():
# The CUBA example with sound!
taum = 20 * ms
taue = 5 * ms
taui = 10 * ms
Vt = -50 * mV
Vr = -60 * mV
El = -49 * mV
eqs = Equations('''
dv/dt = (ge+gi-(v-El))/taum : volt
dge/dt = -ge/taue : volt
dgi/dt = -gi/taui : volt
''')
P = NeuronGroup(4000, model=eqs, threshold=Vt, reset=Vr, refractory=5 * ms)
P.v = Vr
P.ge = 0 * mV
P.gi = 0 * mV
Pe = P.subgroup(3200)
Pi = P.subgroup(800)
we = (60 * 0.27 / 10) * mV # excitatory synaptic weight (voltage)
wi = (-20 * 4.5 / 10) * mV # inhibitory synaptic weight
Ce = Connection(Pe, P, 'ge', weight=we, sparseness=0.5)
Ci = Connection(Pi, P, 'gi', weight=wi, sparseness=0.5)
P.v = Vr + rand(len(P)) * (Vt - Vr)
# Record the number of spikes
M = SoundMonitor(P)
run(10 * second)
def test_synfire():
from brian import *
# Neuron model parameters
Vr = -70 * mV
Vt = -55 * mV
taum = 10 * ms
taupsp = 0.325 * ms
weight = 4.86 * mV
# Neuron model
eqs = Equations('''
dV/dt=(-(V-Vr)+x)*(1./taum) : volt
dx/dt=(-x+y)*(1./taupsp) : volt
dy/dt=-y*(1./taupsp)+25.27*mV/ms+\
(39.24*mV/ms**0.5)*xi : volt
''')
# Neuron groups
P = NeuronGroup(N=1000, model=eqs,
threshold=Vt, reset=Vr, refractory=1 * ms)
Pinput = PulsePacket(t=50 * ms, n=85, sigma=1 * ms)
# The network structure
Pgp = [ P.subgroup(100) for i in range(10)]
C = Connection(P, P, 'y')
for i in range(9):
C.connect_full(Pgp[i], Pgp[i + 1], weight)
Cinput = Connection(Pinput, Pgp[0], 'y')
Cinput.connect_full(weight=weight)
monitor = SoundMonitor(P)
# Setup the network, and run it
P.V = Vr + rand(len(P)) * (Vt - Vr)
run(1*second)
# Plot result
show()
if __name__ == '__main__':
test_synfire()