cochlea3 is a collection of inner ear models. All models are easily accessible as Python3 functions. They take sound signal as input and return spike trains of the auditory nerve fibers:
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Sound Spike Trains
(Auditory Nerve)
The package contains state-of-the-art biophysical models, which give realistic approximation of the auditory nerve activity.
Whenever possible, the models were implemented using the original code from their authors. As a result, they provide the same responses as the original models. In most cases, it was verified by the unit testing (see tests directory for details).
The implementation is also fast. It is easy to generate responses of hundreds or even thousands of auditory nerve fibers (ANFs). For example, one can generate responses of the whole human auditory nerve (around 30,000 ANFs). The models were usually tested with sounds of up to 1 second in duration.
cochlea3 is derived from cochlea but with Python 3 support and some minor changes.
I developed cochlea during my PhD in the group of Werner Hemmert (Bio-Inspired Information Processing) at the TUM.
- State of the art inner ear models accessible from Python 3.
- Contains full biophysical inner ear models: sound in, spikes out.
- Fast; can generate thousands of spike trains.
- Can be used with with neuron simulation software such as NEURON or Brian.
- Holmberg, M. (2007). Speech Encoding in the Human Auditory Periphery: Modeling and Quantitative Assessment by Means of Automatic Speech Recognition. PhD thesis, Technical University Darmstadt.
- Zilany, M. S., Bruce, I. C., Nelson, P. C., & Carney, L. H. (2009). A phenomenological model of the synapse between the inner hair cell and auditory nerve: long-term adaptation with power-law dynamics. The Journal of the Acoustical Society of America, 126(5), 2390-2412.
- Zilany, M. S., Bruce, I. C., & Carney, L. H. (2014). Updated parameters and expanded simulation options for a model of the auditory periphery. The Journal of the Acoustical Society of America, 135(1), 283-286.
Initialize the modules:
import cochlea3
Generate sound:
fs = 100e3
sound = wv.ramped_tone(
fs=fs,
freq=1000,
duration=0.1,
dbspl=50
)
Run the model (responses of 200 cat HSR fibers):
anf_trains = cochlea.run_zilany2014(
sound,
fs,
anf_num=(200,0,0),
cf=1000,
seed=0,
species='cat'
)
Plot the results:
th.plot_raster(anf_trains) th.show()
More examples are available in examples directory.
pip3 install cochlea3
Check INSTALL.rst for more details.
All models return spike trains in a common format. The format is based on standard Python data structures (list, dict) and Numpy arrays. It contains of a list of dicts where each dict contains standard keys: 'type', 'cf', 'offset', 'duration', 'spikes'.
Spike train data format is based on a standard DataFrame format from the excellent pandas library. Spike trains and their meta data are stored in DataFrame, where each row corresponds to a single neuron:
| index | duration | type | cf | spikes |
|---|---|---|---|---|
| 0 | 0.15 | hsr | 8000 | [0.00243, 0.00414, 0.00715, 0.01089, 0.01358, ... |
| 1 | 0.15 | hsr | 8000 | [0.00325, 0.01234, 0.0203, 0.02295, 0.0268, 0.... |
| 2 | 0.15 | hsr | 8000 | [0.00277, 0.00594, 0.01104, 0.01387, 0.0234, 0... |
| 3 | 0.15 | hsr | 8000 | [0.00311, 0.00563, 0.00971, 0.0133, 0.0177, 0.... |
| 4 | 0.15 | hsr | 8000 | [0.00283, 0.00469, 0.00929, 0.01099, 0.01779, ... |
| 5 | 0.15 | hsr | 8000 | [0.00352, 0.00781, 0.01138, 0.02166, 0.02575, ... |
| 6 | 0.15 | hsr | 8000 | [0.00395, 0.00651, 0.00984, 0.0157, 0.02209, 0... |
| 7 | 0.15 | hsr | 8000 | [0.00385, 0.009, 0.01537, 0.02114, 0.02377, 0.... |
The column 'spikes' is the most important and stores an array with spike times (time stamps) in seconds for every action potential. The column 'duration' is the duration of the sound. The column 'cf' is the characteristic frequency (CF) of the fiber. The column 'type' tells us what auditory nerve fiber generated the spike train. 'hsr' is for high-spontaneous rate fiber, 'msr' and 'lsr' for medium- and low-spontaneous rate fibers.
Advantages of the format:
easy addition of new meta data,
efficient grouping and filtering of trains using DataFrame functionality,
export to MATLAB struct array through mat files:
scipy.io.savemat( "spikes.mat", {'spike_trains': spike_trains.to_records()} )
The library thorns has more information and functions to manipulate spike trains.
- Open tasks: TODO.org (best viewed in Emacs org-mode)
- Issue Tracker: https://github.com/mrkrd/cochlea/issues
- Source Code: https://github.com/mrkrd/cochlea
Rudnicki M., Schoppe O., Isik M., Völk F. and Hemmert W. (2015). Modeling auditory coding: from sound to spikes. Cell and Tissue Research, Springer Nature, 361, pp. 159—175. doi:10.1007/s00441-015-2202-z https://link.springer.com/article/10.1007/s00441-015-2202-z
BibTeX entry:
@Article{Rudnicki2015,
author = {Marek Rudnicki and Oliver Schoppe and Michael Isik and Florian Völk and Werner Hemmert},
title = {Modeling auditory coding: from sound to spikes},
journal = {Cell and Tissue Research},
year = {2015},
volume = {361},
number = {1},
pages = {159--175},
month = {jun},
doi = {10.1007/s00441-015-2202-z},
publisher = {Springer Nature},
}
Do not forget to cite the original authors of the models as listed in Implemented Models.
We would like to thank Muhammad S.A. Zilany, Ian C. Bruce and Laurel H. Carney for developing inner ear models and allowing us to use their code in cochlea.
Thanks goes to Marcus Holmberg, who developed the traveling wave based model. His work was supported by the General Federal Ministry of Education and Research within the Munich Bernstein Center for Computational Neuroscience (reference No. 01GQ0441, 01GQ0443 and 01GQ1004B).
We are grateful to Ray Meddis for support with the Matlab Auditory Periphery model.
And last, but not least, I would like to thank Werner Hemmert for supervising my PhD. The thesis entitled Computer models of acoustical and electrical stimulation of neurons in the auditory system can be found at https://mediatum.ub.tum.de/1445042
This work was supported by the General Federal Ministry of Education and Research within the Munich Bernstein Center for Computational Neuroscience (reference No. 01GQ0441 and 01GQ1004B) and the German Research Foundation Foundation's Priority Program PP 1608 Ultrafast and temporally precise information processing: Normal and dysfunctional hearing.
The project is licensed under the GNU General Public License v3 or later (GPLv3+).