def main():
tmax = 20e-3
fs = 500e3
sg.set_fs(fs)
sg.set_celsius(37)
# Acoustic stimmulation
anf_ac = sg.ANF_Axon(record_voltages=True)
h.topology()
h.psection(sec=anf_ac.get_sections()[0])
anf_ac.vesicles = [2e-3, 5e-3]
# Electrical stimulation
# set-up ANF
anf_el = sg.ANF_Axon(record_voltages=True)
# anf_el.set_geometry('straight', x0=250e-6, y0=500e-6, z0=0)
anf_el.set_geometry('bent', a=750e-6, b=500e-6, z=0)
# set-up electrode
el = sg.Electrode()
el.z = 0
stim = np.zeros(int(tmax * fs))
stim[int(tmax / 3 * fs):int((tmax / 3 + 1e-3) * fs)] = -0.2e-3 # (A)
el.fs = fs
el.stim = stim
# `connect' ANF and electrode
anf_el.electrodes = [el]
# Run
sg.run(tmax, [anf_ac, anf_el])
# Plot
print(anf_ac.get_spikes(), anf_el.get_spikes())
sg.plot_vectors(anf_ac.get_voltages()[:, 0:6], fs)
sg.plot_vectors(anf_el.get_voltages()[:, 0:6], fs)
sg.plot_geometry([anf_el, el])
plt.show()
def main():
fs = 500e3
anf_num = 100
electrode_num = 12
tmax = 0.1
sg.set_fs(fs)
sg.set_celsius(37)
stim = np.zeros(tmax * fs)
electrodes = []
for i in range(electrode_num):
electrode = sg.Electrode()
electrode.fs = fs
electrode.stim = stim
anfs = []
for i in range(anf_num):
anf = sg.ANF_Axon()
anf.electrodes = electrodes
anfs.append(anf)
sg.run(tmax, anfs)
def run_holmberg2007_sg(
sound,
fs,
anf_num,
seed,
cf=None,
weight=None,
channels='schwarz1987_pure',
):
"""Run inner ear model by [Holmberg2007]_ in quantal mode combined
with spiral ganglion neurons.
Parameters
----------
weight : float
Synaptic weight of the IHC synapse.
channels : str or dict
Channels used in the ANF model.
"""
vesicle_trains = cochlea.run_holmberg2007_vesicles(
sound=sound,
fs=fs,
anf_num=anf_num,
seed=seed,
cf=cf,
)
duration = th.get_duration(vesicle_trains)
sg.set_celsius(37)
sg.set_fs(100e3)
anfs = []
for _, train in vesicle_trains.iterrows():
anf = sg.ANF_Axon(
weight=weight,
channels=channels,
meta={
'type': train['type'],
'cf': train['cf']
},
)
anf.vesicles = train['vesicles']
anfs.append(anf)
sg.run(duration=duration, anfs=anfs)
trains = []
for anf in anfs:
trains.append(anf.get_trains())
trains = pd.concat(trains)
return trains
def make_anf_electrode():
h.celsius = 37
electrode = sg.Electrode()
electrode.x = 300e-6
anf = sg.ANF_Axon()
# anf.set_geometry('bent', a=750, b=500, z=0)
anf.set_geometry('straight', x0=0, y0=500e-6, z0=0)
return anf, electrode
def test_get_segment_path_positions():
anf = sg.ANF_Axon(nodes=2)
anf.set_geometry('straight', x0=0, y0=0, z0=0)
# default section length (L): [10, 250, 1, 250, ...]
expected = np.array([
5 * 1e-6,
10e-6 + 250e-6/2,
10e-6 + 250e-6 + 1e-6/2,
10e-6 + 250e-6 + 1e-6 + 250e-6/2
])
actual = anf.get_segment_path_positions()
assert_equal(actual, expected)
def _simulate_anf_at((z, electrodes, return_voltages)):
logger.info("Calculating ANF at z = {} [m]".format(z))
sg.set_fs(500e3)
sg.set_celsius(37)
anf = sg.ANF_Axon(record_voltages=return_voltages)
anf.set_geometry('straight', x0=0, y0=500e-6, z0=z)
anf.electrodes = electrodes
tmax = max([len(el.stim) for el in electrodes])
duration = tmax / electrodes[0].fs
sg.run(duration=duration, anfs=[anf])
if return_voltages:
return anf.get_voltages()
else:
return anf.get_trains()