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::
+----------+ __|______|______|____.-. .-. .-. | |--> |____|__|__ / \ / \ / \ -->| Cochlea3 |--> |__|__| '-' '-' | |--> |____|__|____ +----------+ 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.
.. _spike trains: https://en.wikipedia.org/wiki/Spike_train
.. _Bio-Inspired Information Processing: https://www.ei.tum.de/en/bai/home/
.. _NEURON: http://www.neuron.yale.edu/neuron/ .. _Brian: http://briansimulator.org/
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.
.. _examples: ./examples
::
pip3 install cochlea3
Check INSTALL.rst_ for more details.
.. _INSTALL.rst: ./INSTALL.rst
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:
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
.. _TODO.org: TODO.org
Carney Lab_Matlab Auditory Periphery_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/
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+).