You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
103 lines
3.0 KiB
103 lines
3.0 KiB
2 years ago
|
"""
|
||
|
Test the embedded auditory nerve model with a set of tone pips.
|
||
|
(Zilany et al. 2014; requires MATLAB)
|
||
|
|
||
|
This demonstrates the lowest-level access to the auditory nerve model that is
|
||
|
available in the cnmodel API. For higher-level tools, see test_sound_stim.py
|
||
|
(which uses an_model.get_spiketrain) and test_sgc_input.py (which uses
|
||
|
cells.DummySGC).
|
||
|
|
||
|
Usage:
|
||
|
python test_an_model.py
|
||
|
(no arguments)
|
||
|
|
||
|
(Adapted from makeANF_CF_RI.m)
|
||
|
"""
|
||
|
import time
|
||
|
import numpy as np
|
||
|
import pyqtgraph as pg
|
||
|
from cnmodel import an_model
|
||
|
from cnmodel.util import sound
|
||
|
|
||
|
|
||
|
def test_an_model():
|
||
|
# model fiber parameters
|
||
|
CF = 1.5e3 # CF in Hz
|
||
|
cohc = 1.0 # normal ohc function
|
||
|
cihc = 1.0 # normal ihc function
|
||
|
species = (
|
||
|
1
|
||
|
) # 1 for cat (2 for human with Shera et al. tuning 3 for human with Glasberg & Moore tuning)
|
||
|
noiseType = 1 # 1 for variable fGn (0 for fixed fGn)
|
||
|
fiberType = (
|
||
|
3
|
||
|
) # spontaneous rate (in spikes/s) of the fiber BEFORE refractory effects "1" = Low "2" = Medium "3" = High
|
||
|
implnt = (
|
||
|
0
|
||
|
) # "0" for approximate or "1" for actual implementation of the power-law functions in the Synapse
|
||
|
|
||
|
# stimulus parameters
|
||
|
F0 = CF # stimulus frequency in Hz
|
||
|
Fs = 100e3 # sampling rate in Hz (must be 100, 200 or 500 kHz)
|
||
|
T = 150e-3 # stimulus duration in seconds
|
||
|
pdur = 0.02 # pip duration
|
||
|
pstart = [0.01, 0.035] # pip start times
|
||
|
rt = 2.5e-3 # rise/fall time in seconds
|
||
|
stimdb = 65 # stimulus intensity in dB SPL
|
||
|
|
||
|
# PSTH parameters
|
||
|
nrep = 50 # number of stimulus repetitions (e.g., 50)
|
||
|
psthbinwidth = 0.5e-3 # binwidth in seconds
|
||
|
|
||
|
stim = sound.TonePip(
|
||
|
rate=Fs,
|
||
|
duration=T,
|
||
|
f0=F0,
|
||
|
dbspl=stimdb,
|
||
|
pip_duration=pdur,
|
||
|
pip_start=pstart,
|
||
|
ramp_duration=rt,
|
||
|
)
|
||
|
t = stim.time
|
||
|
pin = stim.sound
|
||
|
db = stim.measure_dbspl(rt, T - rt)
|
||
|
|
||
|
an_model.seed_rng(34978)
|
||
|
start = time.time()
|
||
|
vihc = an_model.model_ihc(
|
||
|
pin, CF, nrep, 1 / Fs, T + 1e-3, cohc, cihc, species
|
||
|
) # , _transfer=False)
|
||
|
print("IHC time:", time.time() - start)
|
||
|
start = time.time()
|
||
|
m, v, psth = an_model.model_synapse(
|
||
|
vihc, CF, nrep, 1 / Fs, fiberType, noiseType, implnt
|
||
|
)
|
||
|
print("Syn time:", time.time() - start)
|
||
|
|
||
|
win = pg.GraphicsWindow()
|
||
|
p1 = win.addPlot(title="Input Stimulus (%0.1f dBSPL)" % db)
|
||
|
p1.plot(t, pin)
|
||
|
|
||
|
p2 = win.addPlot(col=0, row=1, title="IHC voltage")
|
||
|
p2.setXLink(p1)
|
||
|
# vihc = vihc.get()[0]
|
||
|
vihc = vihc[: len(vihc) // nrep]
|
||
|
t = np.arange(len(vihc)) * 1e-5
|
||
|
p2.plot(t, vihc)
|
||
|
|
||
|
p3 = win.addPlot(col=0, row=2, title="ANF PSTH")
|
||
|
p3.setXLink(p2)
|
||
|
ds = 100
|
||
|
size = psth.size // ds
|
||
|
psth = psth[: size * ds].reshape(size, ds).sum(axis=1)
|
||
|
t = np.arange(len(psth)) * 1e-5 * ds
|
||
|
p3.plot(t, psth[:-1], stepMode=True, fillLevel=0, fillBrush="w")
|
||
|
if sys.flags.interactive == 0:
|
||
|
pg.QtGui.QApplication.exec_()
|
||
|
|
||
|
|
||
|
if __name__ == "__main__":
|
||
|
import sys
|
||
|
|
||
|
test_an_model()
|