Metadata-Version: 1.1
Name: cochlea
Version: 1.1
Summary: Inner ear models in Python
Home-page: https://github.com/mrkrd/cochlea
Author: Marek Rudnicki
Author-email: marek.rudnicki@tum.de
License: GPLv3+
Download-URL: https://github.com/mrkrd/cochlea/tarball/master
Description: cochela
        =======
        
        *Inner ear models in Python*
        
        
        :Name: cochela
        :Author: Marek Rudnicki
        :Email: marek.rudnicki@tum.de
        :URL: https://github.com/mrkrd/cochlea
        :Documentation: https://pythonhosted.org/cochlea/
        :License: GNU General Public License v3 or later (GPLv3+)
        
        
        
        Description
        -----------
        
        *cochlea* is a collection of inner ear models.  The 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
        
        
        
        The implemented models are state-of-the-art biophysical models and
        give realistic approximation of the auditory nerve activity.
        
        They are implemented using the original code from their authors
        whenever possible.  We wanted to have implementations that gives the
        same results as the original models.  We also made an effort to verify
        the implementation with unit testing (tests directory).
        
        The models are 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`_
        at TUM.  It went through several versions and rewrites.  Now, it is
        quite stable and we decided to release it for the community.
        
        *cochlea* could be used by:
        
        - researchers doing computational neuroscience (combines well with
          NEURON_ and Brian_),
        - experimenters that require realistic input spike trains,
        - researchers improving inner ear models,
        - everyone interested in understanding how hearing works.
        
        
        .. _`Werner Hemmert`: http://www.imetum.tum.de/research/bai/home/?L=1
        .. _NEURON: http://www.neuron.yale.edu/neuron/
        .. _Brian: http://briansimulator.org/
        
        
        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_wrapper: https://github.com/mrkrd/matlab_wrapper
        
        
        
        Usage
        -----
        
        Check our online DEMO_!
        
        
        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()
        
        
        
        
        .. _DEMO: http://nbviewer.ipython.org/github/mrkrd/cochlea/blob/master/examples/cochlea_demo.ipynb
        
        
        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()}
            )
        
        Please, check thorns_ for 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
        
        
        Requirements
        ------------
        
        - Python (2.7)
        - Numpy
        - Scipy
        - Cython
        - Pandas
        
        - Matplotlib (optional, for examples)
        - docopt (optional, for the command line scripts)
        - thorns_ (optional, for examples and stats)
        - matlab_wrapper_ (optional, for the MAP external model)
        
        
        Note: On Windows you can install a Python distribution such as
        Anaconda_ or `Python(x,y)`_ to fulfill most of the dependencies.
        
        
        .. _thorns: https://github.com/mrkrd/thorns
        .. _matlab_wrapper: https://github.com/mrkrd/matlab_wrapper
        .. _Anaconda: https://store.continuum.io/cshop/anaconda/
        .. _`Python(x,y)`: https://code.google.com/p/pythonxy/
        
        
        Installation
        ------------
        
        Quick install::
        
          pip install cochlea
        
        
        Note: Windows binary packages (wheels) are coming soon.  At the moment
        you need `cython and a working compiler`_ to install the package.
        
        .. _`cython and a working compiler`: https://github.com/cython/cython/wiki/64BitCythonExtensionsOnWindows
        
        
        
        Issues and Bugs
        ---------------
        
        https://github.com/mrkrd/cochlea/issues
        
        
        
        
        Other implementations
        ---------------------
        
        - `Carney Lab`_
        - `Matlab Auditory Periphery`_
        - DSAM_
        - `Brian Hears`_
        
        .. _`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
        
        
        
        
        Citing
        ------
        
        If you are using this software in your research, please make a
        reference: Rudnicki, M. and Hemmert, W. (2014) *Cochlea: inner ear
        models in Python*, https://github.com/mrkrd/cochlea.
        
        When you use any of the models, always cite the original publications
        describing the model.
        
        
        
        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.
        
        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.
        
        
        
        .. _`Werner Hemmert`: http://www.imetum.tum.de/research/bai/home/?L=1
        
Platform: Linux
Platform: Windows
Platform: FreeBSD
Platform: OSX
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Operating System :: POSIX
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Cython
Classifier: Programming Language :: C
