def main():
fs = 48e3
cf = cochlea.get_nearest_cf_holmberg2007(1e3)
### Make sound
sound = wv.ramped_tone(
fs=fs,
freq=cf,
duration=150e-3,
pad=10e-3,
dbspl=70,
)
### Run model
anf_trains = sg.run_holmberg2007_sg(
sound,
fs,
cf=cf,
anf_num=(10, 0, 0),
seed=0,
)
print(th.firing_rate(anf_trains))
### Plot auditory nerve response
fig, ax = plt.subplots(2, 1)
th.plot_signal(signal=sound, fs=fs, ax=ax[0])
th.plot_raster(anf_trains, ax=ax[1])
plt.show()
def _run_model(model, dbspl, cf, model_pars, tone_duration):
onset = 10e-3
assert tone_duration > onset
fs = model_pars.setdefault('fs', 100e3)
model_pars.setdefault('anf_num', (250,250,250))
model_pars.setdefault('seed', 0)
sound = wv.ramped_tone(
fs=fs,
freq=cf,
duration=tone_duration,
pad=0,
dbspl=dbspl
)
anf = model(
sound=sound,
cf=cf,
**model_pars
)
rates = {}
for typ,trains in anf.groupby('type'):
trimmed = th.trim(trains, onset, None)
rate = th.firing_rate(trimmed)
rates[typ] = rate
return rates
def main():
fs = 48e3
tmax = 0.1
### Make sound
t = np.arange(0, tmax, 1/fs)
s = np.zeros_like(t)
### Run model
vesicle_trains = cochlea.run_holmberg2007_vesicles(
s,
fs,
anf_num=(1,0,0),
seed=0,
)
print(vesicle_trains)
### Need to rename a column: vesicles -> spikes, because that's
### the expected name by many functions in thorns
trains = vesicle_trains.rename(columns={'vesicles': 'spikes'})
### Calculate average rate
rate = th.firing_rate(trains)
print()
print("Spontanious rate:", rate)
### Raster plot
th.plot_raster(trains)
th.show()
def main():
fs = 48e3
tmax = 0.1
### Make sound
t = np.arange(0, tmax, 1/fs)
s = np.zeros_like(t)
### Run model
vesicle_trains = cochlea.run_holmberg2007_vesicles(
s,
fs,
anf_num=(1,0,0),
seed=0,
)
print(vesicle_trains)
### Need to rename a column: vesicles -> spikes, because that's
### the expected name by many functions in thorns
trains = vesicle_trains.rename(columns={'vesicles': 'spikes'})
### Calculate average rate
rate = th.firing_rate(trains)
print()
print("Spontanious rate:", rate)
### Raster plot
th.plot_raster(trains)
th.show()
def _run_model(model, dbspl, cf, model_pars, tone_duration):
onset = 10e-3
assert tone_duration > onset
fs = model_pars.setdefault('fs', 100e3)
model_pars.setdefault('anf_num', (250,250,250))
model_pars.setdefault('seed', 0)
sound = wv.ramped_tone(
fs=fs,
freq=cf,
duration=tone_duration,
pad=0,
dbspl=dbspl
)
anf = model(
sound=sound,
cf=cf,
**model_pars
)
### TODO: try to use DataFrame.groupby instead
hsr = anf.query("type == 'hsr'")
hsr = th.trim(hsr, onset, None)
rate_hsr = th.firing_rate(hsr)
msr = anf.query("type == 'msr'")
msr = th.trim(msr, onset, None)
rate_msr = th.firing_rate(msr)
lsr = anf.query("type == 'lsr'")
lsr = th.trim(lsr, onset, None)
rate_lsr = th.firing_rate(lsr)
rates = {
'hsr': rate_hsr,
'msr': rate_msr,
'lsr': rate_lsr,
}
return rates
def _run_model(model, dbspl, cf, model_pars):
duration = 100e-3
onset = 10e-3
fs = model_pars.setdefault('fs', 100e3)
model_pars.setdefault('anf_num', (250,250,250))
model_pars.setdefault('seed', 0)
sound = wv.ramped_tone(
fs=fs,
freq=cf,
duration=duration,
pad=0,
dbspl=dbspl
)
anf = model(
sound=sound,
cf=cf,
**model_pars
)
hsr = anf[anf['type']=='hsr']
hsr = th.trim(hsr, onset, None)
rate_hsr = th.firing_rate(hsr)
msr = anf[anf['type']=='msr']
msr = th.trim(msr, onset, None)
rate_msr = th.firing_rate(msr)
lsr = anf[anf['type']=='lsr']
lsr = th.trim(lsr, onset, None)
rate_lsr = th.firing_rate(lsr)
rates = {
'hsr': rate_hsr,
'msr': rate_msr,
'lsr': rate_lsr,
}
return rates
def test_firing_rate():
trains = th.make_trains(
[[0.1, 0.4],
[0.4, 0.5, 0.6]],
duration=1
)
rate = th.firing_rate(
trains
)
assert_equal(rate, 2.5)
def error_func(
dbspl,
model,
cf,
spont_rate,
model_pars,
asr_filter=False,
freq=None
):
pars = dict(model_pars)
fs = pars.setdefault('fs', 100e3)
pars.setdefault('seed', 0)
pars.setdefault('anf_num', (1000, 0, 0))
if freq is None:
freq = cf
tone_duration = 250e-3
onset = 15e-3
sound = wv.ramped_tone(
fs,
freq,
duration=tone_duration,
ramp=2.5e-3,
pad=0,
dbspl=dbspl
)
if asr_filter:
sound = adjust_to_human_thresholds(sound, fs, model)
anf = model(
sound=sound,
cf=cf,
**pars
)
trains = th.trim(anf, onset, None)
rate = th.firing_rate(trains)
error = rate - spont_rate
logging.debug("{} {} {}".format(dbspl, rate, error))
return error
def error_func(dbspl,
model,
cf,
spont_rate,
model_pars,
asr_filter=False,
freq=None):
pars = dict(model_pars)
fs = pars.setdefault('fs', 100e3)
pars.setdefault('seed', 0)
pars.setdefault('anf_num', (1000, 0, 0))
if freq is None:
freq = cf
tone_duration = 250e-3
onset = 15e-3
sound = wv.ramped_tone(fs,
freq,
duration=tone_duration,
ramp=2.5e-3,
pad=0,
dbspl=dbspl)
if asr_filter:
sound = adjust_to_human_thresholds(sound, fs, model)
anf = model(sound=sound, cf=cf, **pars)
trains = th.trim(anf, onset, None)
rate = th.firing_rate(trains)
error = rate - spont_rate
logging.debug("{} {} {}".format(dbspl, rate, error))
return error
def _run_model(model, dbspl, cf, model_pars, tone_duration):
onset = 10e-3
assert tone_duration > onset
fs = model_pars.setdefault('fs', 100e3)
model_pars.setdefault('anf_num', (250, 250, 250))
model_pars.setdefault('seed', 0)
sound = wv.ramped_tone(fs=fs,
freq=cf,
duration=tone_duration,
pad=0,
dbspl=dbspl)
anf = model(sound=sound, cf=cf, **model_pars)
rates = {}
for typ, trains in anf.groupby('type'):
trimmed = th.trim(trains, onset, None)
rate = th.firing_rate(trimmed)
rates[typ] = rate
return rates
'''Plot the results of the inhibitory variation script
You need to previously run the inhibitory_variation.py
script so that the inhibitory_variation.h5 result file
is generated in the work subfolder
'''
import numpy as np
import matplotlib.pyplot as plt
import thorns
import scipy.optimize as opt
res = thorns.util.loaddb('inhibitory_variation')
res = res.reset_index()
calc_fr = lambda x: thorns.firing_rate(thorns.trim(x, start=0.045, stop=0.145))
def gaus(x, a, x0, sigma, b):
return a * np.exp(-(x - x0)**2 / (sigma**2)) + b #
res['rate_left'] = res.spikes_left.apply(calc_fr)
res['rate_right'] = res.spikes_right.apply(calc_fr)
sel_curve = res[res.str_i == res.str_i.unique()[-2]]
fig, ax = plt.subplots(1, 1)
for strength, sg in res.groupby('str_i'):
ax.plot(sg.itd * 1e3, sg.rate_left)
ax.set_ylim(0, 90)
ax.set_ylabel('Firing rate / sps')