def test_make_brian_group():
trains = pd.DataFrame([
{'duration': 100e-3, 'spikes': [10e-3, 30e-3], 'cf': 333},
{'duration': 100e-3, 'spikes': [40e-3, 50e-3], 'cf': 333},
{'duration': 100e-3, 'spikes': [60e-3, 70e-3], 'cf': 333},
])
group = cochlea.make_brian_group(trains)
assert_equal(len(trains), len(group))
def test_make_brian_group():
trains = pd.DataFrame([
{'duration': 100e-3, 'spikes': [10e-3, 30e-3], 'cf': 333},
{'duration': 100e-3, 'spikes': [40e-3, 50e-3], 'cf': 333},
{'duration': 100e-3, 'spikes': [60e-3, 70e-3], 'cf': 333},
])
group = cochlea.make_brian_group(trains)
assert_equal(len(trains), len(group))
def main():
fs = 100e3
cf = 1e3
tmax = 50e-3
sound = wv.ramped_tone(
fs=fs,
freq=cf,
duration=tmax,
pad=30e-3,
dbspl=50,
)
anf_trains = cochlea.run_zilany2014(
sound=sound,
fs=fs,
cf=cf,
anf_num=(300, 0, 0),
seed=0,
species='cat',
)
anfs = cochlea.make_brian_group(anf_trains)
print(anfs)
brainstem = make_brainstem_group(100)
print(brainstem)
monitor = brian.SpikeMonitor(brainstem)
net = brian.Network([brainstem, monitor])
net.run(tmax*second)
brian.raster_plot(monitor)
brian.show()
def main():
fs = 100e3
cf = 1e3
tmax = 50e-3
sound = wv.ramped_tone(
fs=fs,
freq=cf,
duration=tmax,
pad=30e-3,
dbspl=50,
)
anf_trains = cochlea.run_zilany2014(
sound=sound,
fs=fs,
cf=cf,
anf_num=(300, 0, 0),
seed=0,
species='cat',
)
anfs = cochlea.make_brian_group(anf_trains)
print(anfs)
brainstem = make_brainstem_group(100)
print(brainstem)
monitor = brian.SpikeMonitor(brainstem)
net = brian.Network([brainstem, monitor])
net.run(tmax * second)
brian.raster_plot(monitor)
brian.show()
def run_exp(c_freq, itd, str_e, str_i):
# br.globalprefs.set_global_preferences(useweave=True, openmp=True, usecodegen=True,
# usecodegenweave=True )
br.defaultclock.dt = 20E-6 * second
#Basic Parameters
fs_coch = 100e3 # s/second
duration = 100E-3 # seconds##
pad = 20E-3 # second
n_neuron = 300
dbspl = 50
n_neuron = 500
dt_coch = 1 / fs_coch
n_pad = int(pad / dt_coch)
n_itd = int(itd / dt_coch)
const_dt = 100e-6
sound = audio.generate_tone(c_freq, duration, fs_coch)
sound = sound * audio.cosine_fade_window(sound, 20e-3, fs_coch)
sound = coch.set_dbspl(sound, dbspl)
sound = np.concatenate((np.zeros(n_pad), sound, np.zeros(n_pad)))
sound = audio.delay_signal(sound, np.abs(itd), fs_coch)
if itd < 0:
sound = sound[:, ::-1]
duration = len(sound) / fs_coch
# construct ipsi and contra-lateral ANF trains and convert them to
# neuron groups
cochlea_train_left = coch.run_zilany2014(sound=sound[:, 0],
fs=fs_coch,
anf_num=(n_neuron, 0, 0),
cf=c_freq,
species='human',
seed=0)
cochlea_train_right = coch.run_zilany2014(sound=sound[:, 1],
fs=fs_coch,
anf_num=(n_neuron, 0, 0),
cf=c_freq,
species='human',
seed=0)
anf_group_left = coch.make_brian_group(cochlea_train_left)
anf_group_right = coch.make_brian_group(cochlea_train_right)
# Setup a new mso group and new gbc groups
mso_group_left = make_mso_group(n_neuron)
mso_group_right = make_mso_group(n_neuron)
gbc_group_left = cochlea_to_gbc(anf_group_left, n_neuron)
gbc_group_right = cochlea_to_gbc(anf_group_right, n_neuron)
# Synaptic connections for the groups
syn_mso_l_in_ipsi, syn_mso_l_in_contra = inhibitory_to_mso(
mso_group=mso_group_left,
ipsi_group=gbc_group_left['neuron_groups'][0],
contra_group=gbc_group_right['neuron_groups'][0],
strength=str_i,
ipsi_delay=0,
contra_delay=-0.6e-3 + const_dt)
syn_mso_r_in_ipsi, syn_mso_r_in_contra = inhibitory_to_mso(
mso_group=mso_group_right,
ipsi_group=gbc_group_right['neuron_groups'][0],
contra_group=gbc_group_left['neuron_groups'][0],
strength=str_i,
ipsi_delay=0,
contra_delay=-0.6e-3 + const_dt)
syn_mso_l_ex_ipsi, syn_mso_l_ex_contra = excitatory_to_mso(
mso_group=mso_group_left,
ipsi_group=anf_group_left,
contra_group=anf_group_right,
strength=str_e,
contra_delay=const_dt)
syn_mso_r_ex_ipsi, syn_mso_r_ex_contra = excitatory_to_mso(
mso_group=mso_group_right,
ipsi_group=anf_group_right,
contra_group=anf_group_left,
strength=str_e,
contra_delay=const_dt)
sp_mon_left = br.SpikeMonitor(mso_group_left, record=True)
sp_mon_right = br.SpikeMonitor(mso_group_right, record=True)
network = ([
mso_group_left, mso_group_right, anf_group_left, anf_group_right,
syn_mso_l_ex_ipsi, syn_mso_l_ex_contra, syn_mso_l_in_ipsi,
syn_mso_l_in_contra, syn_mso_r_ex_ipsi, syn_mso_r_ex_contra,
syn_mso_r_in_ipsi, syn_mso_r_in_contra, sp_mon_left, sp_mon_right
] + gbc_group_left['neuron_groups'] + gbc_group_right['neuron_groups'])
run(duration, network)
mso_train_left = thorns.make_trains(sp_mon_left)
mso_train_right = thorns.make_trains(sp_mon_right)
return {'spikes_left': mso_train_left, 'spikes_right': mso_train_right}
