cochlea

cochlea is a collection of inner ear models. All models are easily accessible as Python functions. They take sound signal as input and return spike trains_ of the auditory nerve fibers::

                       +-----------+     __|______|______|____

.-. .-. .-. | |--> ||| / \ / \ / \ -->| Cochlea |--> ||| '-' '-' | |--> |____|__| +-----------+ Sound Spike Trains (Auditory Nerve)

The package contains state-of-the-art biophysical models, which give realistic approximation of the auditory nerve activity.

The models are implemented using the original code from their authors whenever possible. Therefore, they return the same results as the original models. We made an effort to verify it with 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). It is possible, for example, to generate responses of the whole human auditory nerve (around 30,000 ANFs). We usually tested the models with sounds up to 1 second in duration.

I developed cochlea during my PhD in the group of Werner Hemmert (Bio-Inspired Information Processing_) at the TUM. It went through several versions and rewrites. Now, it is quite stable and we decided to release it for the community.

.. _spike trains: https://en.wikipedia.org/wiki/Spike_train .. _Bio-Inspired Information Processing: https://www.ei.tum.de/en/bai/home/

Features

• State of the art inner ear models accessible from Python.
• Contains full biophysical inner ear models: sound in, spikes out.
• Fast; can generate thousands of spike trains.
• Interoperability with neuron simulation software such as NEURON_ and Brian_.

.. _NEURON: http://www.neuron.yale.edu/neuron/ .. _Brian: http://briansimulator.org/

Implemented Models

• 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.
• MATLAB Auditory Periphery_ by Meddis et al. (external model, not implemented in the package, but easily accessible through matlab_wrapper_).

.. _MATLAB Auditory Periphery: http://www.essexpsychology.macmate.me/HearingLab/modelling.html .. _matlab_wrapper: https://github.com/mrkrd/matlab_wrapper

Usage

Check our online DEMO_ and examples_ (probably the easiest is to start with run_zilany2014.py_).

Initialize the modules::

import cochlea import thorns as th import thorns.waves as wv

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()

You can browse through the API documentation at: https://pythonhosted.org/cochlea/

.. _DEMO: https://github.com/mrkrd/cochlea/tree/master/examples/cochlea_demo.ipynb .. _examples: https://github.com/mrkrd/cochlea/tree/master/examples .. _run_zilany2014.py: https://github.com/mrkrd/cochlea/blob/master/examples/run_zilany2014.py

Installation

::

pip install cochlea

Check INSTALL.rst_ for details.

.. _INSTALL.rst: INSTALL.rst

Spike Train Format

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.

.. _DataFrame: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html .. _pandas: http://pandas.pydata.org/ .. _thorns: https://github.com/mrkrd/thorns

Contribute & Support

• 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

.. _TODO.org: TODO.org

Similar Projects

• Carney Lab_
• Matlab Auditory Periphery_
• DSAM_
• Brian Hears_
• The Auditory Modeling Toolbox_

.. _Carney Lab: http://www.urmc.rochester.edu/labs/Carney-Lab/publications/auditory-models.cfm .. _DSAM: http://dsam.org.uk/ .. _Matlab Auditory Periphery: http://www.essexpsychology.macmate.me/HearingLab/modelling.html .. _Brian Hears: http://www.briansimulator.org/docs/hears.html .. _The Auditory Modeling Toolbox: http://amtoolbox.sourceforge.net/

Citing

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.

Acknowledgments

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.

License

The project is licensed under the GNU General Public License v3 or later (GPLv3+).