def main():
fs = 48e3
### Make sound
tmax = 0.03
t = np.arange(0, tmax, 1 / fs)
s = dsp.chirp(t, 80, t[-1], 16000)
sound = cochlea.set_dbspl(s, 50)
### Run model
anf = run_matlab_auditory_periphery(
sound,
fs,
anf_num=(100, 50, 20),
cf=(125, 16000, 80),
seed=0,
)
### Accumulate spike trains
anf_acc = th.accumulate(anf, keep=['cf', 'duration'])
anf_acc.sort('cf', ascending=False, inplace=True)
### Plot auditory nerve response
fig, ax = plt.subplots(2, 1, sharex=True)
th.plot_signal(signal=sound, fs=fs, ax=ax[0])
th.plot_neurogram(anf_acc, fs, ax=ax[1])
plt.show()
def main():
fs = 100e3
### Make sound
t = np.arange(0, 0.1, 1/fs)
s = dsp.chirp(t, 80, t[-1], 20000)
s = cochlea.set_dbspl(s, 50)
s = np.concatenate( (s, np.zeros(10e-3 * fs)) )
### Run model
rates = cochlea.run_zilany2014_rate(
s,
fs,
anf_types=['msr'],
cf=(125, 20000, 100),
powerlaw='approximate',
species='human'
)
### Plot rates
fig, ax = plt.subplots()
img = ax.imshow(
rates.T,
aspect='auto'
)
plt.colorbar(img)
plt.show()
def main():
fs = 100e3
### Make sound
t = np.arange(0, 0.1, 1 / fs)
s = dsp.chirp(t, 80, t[-1], 20000)
s = cochlea.set_dbspl(s, 50)
pad = np.zeros(10e-3 * fs)
sound = np.concatenate((s, pad))
### Run model
anf = cochlea.run_zilany2014(
sound, fs, anf_num=(100, 0, 0), cf=(125, 20000, 100), seed=0, powerlaw="approximate", species="human"
)
### Accumulate spike trains
anf_acc = th.accumulate(anf, keep=["cf", "duration"])
anf_acc.sort("cf", ascending=False, inplace=True)
### Plot auditory nerve response
fig, ax = plt.subplots(2, 1)
th.plot_signal(signal=sound, fs=fs, ax=ax[0])
th.plot_neurogram(anf_acc, fs, ax=ax[1])
plt.show()
def main():
fs = 48e3
### Make sound
t = np.arange(0, 0.1, 1 / fs)
s = dsp.chirp(t, 80, t[-1], 20000)
s = cochlea.set_dbspl(s, 50)
s = np.concatenate((s, np.zeros(int(10e-3 * fs))))
### Run model
anf_trains = cochlea.run_holmberg2007(
s,
fs,
anf_num=(100, 0, 0),
seed=0,
)
### Plot auditory nerve response
anf_acc = th.accumulate(anf_trains, keep=['cf', 'duration'])
anf_acc.sort('cf', ascending=False, inplace=True)
fig, ax = plt.subplots(2, 1)
th.plot_signal(signal=s, fs=fs, ax=ax[0])
th.plot_neurogram(anf_acc, fs, ax=ax[1])
plt.show()
def main():
fs = 100e3
cf = 8e3
### Make sound
t = np.arange(0, 0.1, 1/fs)
tone = np.sin(2*np.pi*t*cf)
tone = cochlea.set_dbspl(tone, 20)
pad = np.zeros(50e-3 * fs)
sound = np.concatenate( (tone, pad) )
### Run model
anf = cochlea.run_zilany2014(
sound,
fs,
anf_num=(200,0,0),
cf=cf,
seed=0,
powerlaw='approximate',
species='human'
)
print(anf.head(20))
### Plot PSTH
all_spikes = np.concatenate(anf.spikes)
tmax = anf.duration.max()
bin_size = 1e-3
fig,ax = plt.subplots()
ax.hist(
all_spikes,
bins=int(tmax/bin_size),
range=(0,tmax),
weights=np.ones_like(all_spikes)/bin_size/len(anf)
)
ax.set_xlabel("Time [s]")
ax.set_ylabel("Rate [spikes/s]")
ax.set_title("PSTH")
plt.show()
def main():
fs = 100e3
cf = 8e3
### Make sound
t = np.arange(0, 0.1, 1/fs)
tone = np.sin(2*np.pi*t*cf)
tone = cochlea.set_dbspl(tone, 20)
pad = np.zeros(50e-3 * fs)
sound = np.concatenate( (tone, pad) )
### Run model
anf = cochlea.run_zilany2014(
sound,
fs,
anf_num=(200,0,0),
cf=cf,
seed=0,
powerlaw='approximate',
species='human'
)
print(anf.head(20))
### Plot PSTH
all_spikes = np.concatenate(anf.spikes)
tmax = anf.duration.max()
bin_size = 1e-3
fig,ax = plt.subplots()
ax.hist(
all_spikes,
bins=int(tmax/bin_size),
range=(0,tmax),
weights=np.ones_like(all_spikes)/bin_size/len(anf)
)
ax.set_xlabel("Time [s]")
ax.set_ylabel("Rate [spikes/s]")
ax.set_title("PSTH")
plt.show()
def main():
fs = 100e3
### Make sound
t = np.arange(0, 0.1, 1/fs)
s = dsp.chirp(t, 80, t[-1], 20000)
s = cochlea.set_dbspl(s, 50)
s = np.concatenate( (s, np.zeros(10e-3 * fs)) )
### Run model
anf = cochlea.run_zilany2009(
s,
fs,
anf_num=(100,0,0),
cf=(80, 20000, 100),
seed=0,
powerlaw='approximate'
)
### Plot auditory nerve response
anf_acc = th.accumulate(anf, keep=['cf', 'duration'])
anf_acc.sort('cf', ascending=False, inplace=True)
cfs = anf.cf.unique()
fig, ax = plt.subplots(2,1, sharex=True)
th.plot_neurogram(
anf_acc,
fs,
ax=ax[0]
)
th.plot_raster(
anf[anf.cf==cfs[30]],
ax=ax[1]
)
ax[1].set_title("CF = {}".format(cfs[30]))
plt.show()
def main():
fs = 48e3
### Make sound
tmax = 0.03
t = np.arange(0, tmax, 1/fs)
s = dsp.chirp(t, 80, t[-1], 16000)
sound = cochlea.set_dbspl(s, 50)
### Run model
anf = run_matlab_auditory_periphery(
sound,
fs,
anf_num=(100,50,20),
cf=(125, 16000, 80),
seed=0,
)
### Accumulate spike trains
anf_acc = th.accumulate(anf, keep=['cf', 'duration'])
anf_acc.sort('cf', ascending=False, inplace=True)
### Plot auditory nerve response
fig, ax = plt.subplots(2, 1, sharex=True)
th.plot_signal(
signal=sound,
fs=fs,
ax=ax[0]
)
th.plot_neurogram(
anf_acc,
fs,
ax=ax[1]
)
plt.show()
def main():
fs = 100e3
# Make sound
t = np.arange(0, 0.1, 1/fs)
s = dsp.chirp(t, 80, t[-1], 20000)
s = cochlea.set_dbspl(s, 50)
s = np.concatenate((s, np.zeros(int(10e-3*fs))))
# Run model
anf = cochlea.run_zilany2009(
s,
fs,
anf_num=(100,0,0),
cf=(80, 20000, 100),
seed=0,
powerlaw='approximate'
)
# Plot auditory nerve response
anf_acc = th.accumulate(anf, keep=['cf', 'duration'])
anf_acc.sort_values('cf', ascending=False, inplace=True)
cfs = anf.cf.unique()
fig, ax = plt.subplots(2,1, sharex=True)
th.plot_neurogram(
anf_acc,
fs,
ax=ax[0]
)
th.plot_raster(
anf[anf.cf==cfs[30]],
ax=ax[1]
)
ax[1].set_title("CF = {}".format(cfs[30]))
plt.show()
def main():
fs = 100e3
# Make sound
t = np.arange(0, 0.1, 1/fs)
s = dsp.chirp(t, 80, t[-1], 20000)
s = cochlea.set_dbspl(s, 50)
pad = np.zeros(int(10e-3 * fs))
sound = np.concatenate( (s, pad) )
# Run model
anf = cochlea.run_zilany2014(
sound,
fs,
anf_num=(100,0,0),
cf=(125, 20000, 100),
seed=0,
powerlaw='approximate',
species='human',
)
# Accumulate spike trains
anf_acc = th.accumulate(anf, keep=['cf', 'duration'])
anf_acc.sort_values('cf', ascending=False, inplace=True)
# Plot auditory nerve response
fig, ax = plt.subplots(2,1)
th.plot_signal(
signal=sound,
fs=fs,
ax=ax[0]
)
th.plot_neurogram(
anf_acc,
fs,
ax=ax[1]
)
plt.show()
def main():
fs = 48e3
### Make sound
t = np.arange(0, 0.1, 1/fs)
s = dsp.chirp(t, 80, t[-1], 20000)
s = cochlea.set_dbspl(s, 50)
s = np.concatenate( (s, np.zeros(10e-3 * fs)) )
### Run model
anf_trains = cochlea.run_holmberg2007(
s,
fs,
anf_num=(100,0,0),
seed=0,
)
### Plot auditory nerve response
anf_acc = th.accumulate(anf_trains, keep=['cf', 'duration'])
anf_acc.sort('cf', ascending=False, inplace=True)
fig, ax = plt.subplots(2,1)
th.plot_signal(
signal=s,
fs=fs,
ax=ax[0]
)
th.plot_neurogram(
anf_acc,
fs,
ax=ax[1]
)
plt.show()
def main():
fs = 100e3
### Make sound
t = np.arange(0, 0.1, 1 / fs)
s = dsp.chirp(t, 80, t[-1], 20000)
s = cochlea.set_dbspl(s, 50)
s = np.concatenate((s, np.zeros(10e-3 * fs)))
### Run model
rates = cochlea.run_zilany2014_rate(s,
fs,
anf_types=['msr'],
cf=(125, 20000, 100),
powerlaw='approximate',
species='human')
### Plot rates
fig, ax = plt.subplots()
img = ax.imshow(rates.T, aspect='auto')
plt.colorbar(img)
plt.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}