Metadata-Version: 1.1
Name: fit-neuron
Version: 0.0.6
Summary: Package for estimation and evaluation of neural models from patch clamp neural recordings.
Home-page: http://pythonhosted.org/fit_neuron
Author: Nicolas D. Jimenez
Author-email: nicodjimenez@gmail.com
License: Apache
Description: =============
        fit_neuron
        =============
        
        **fit_neuron** is an easy to use python package for the fast estimation of generalized integrate and fire neural models 
        from patch clamp electrophysiological recordings.  The optimization routines implements a fitting procedure 
        described in [RB2005]_ and [MS2011]_.  The package includes an easy to use interface similar to scikit-learn for fitting models to data and then making predictions with the fitted models.  The routines used can estimate the models described in [RB2005]_, [MN2009]_, and [MS2011]_.  
        As described in depth in the documentation, the subthreshold 
        parameters are estimated using linear regression and the threshold parameters are estimated 
        using maximum likelihood.  The fitting routine is built for speed: it estimates neuron parameters for 10 seconds of data 
        in about 50 seconds on a quad core Asus laptop.  *fit_neuron* also contains efficient implementations 
        of the following spike distance measures: Victor-Purpura [DA2003]_, van Rossum [VR2001]_, Schreiber [SS2003]_, and Gamma [RJ2008]_
        which can be used to evaluate the accuracy of estimated models, as well as provide measures 
        of synchrony between spike trains.  
        
        :Date: 2013-12-28
        :Version: 0.0.5
        :Authors: - Nicolas D. Jimenez
        
        Links 
        ----------
        
        1) **Pypi** 
        
        The latest stable version is available to download at: https://pypi.python.org/pypi/fit_neuron.
        
        2)  **GitHub**
        
        The latest development version is available at: https://github.com/nicodjimenez/fit_neuron.  All relevant contributions are welcome 
        and fast review of pull requests is guaranteed.  
        
        3)  **Documentation**   
        
        Sphinx documentation for this package is available at: http://pythonhosted.org/fit_neuron/.
        
        
        Dependencies
        -------------
        
        1) **Numpy** 
        
        The standard python module for matrix and vector computations: https://pypi.python.org/pypi/numpy.
        
        2) **Scipy** 
        
        The standard python module for statistical analysis: http://www.scipy.org/install.html.
        
        3) **Matplotlib**
        
        The standard python module for data visualization: http://matplotlib.org/users/installing.html.
        
        Installation 
        -----------------------
        
        The fit_neuron package can be installed as follows::
        
        	sudo pip install fit_neuron
        	
        
        The data for the fit_neuron package is then installed as follows::
        
        	sudo python -m fit_neuron.data.dl_neuron_data
        	
        	
        .. warning:: 
        	Running this script for the first time will download a 300 MB zip file containing test recordings 
        	which is then unzipped to over 1 GB of text files in the installation directory of the *fit_neuron* 
        	package.  This may take up to 20 minutes depending on your bandwidth.  After the files are downloaded, the test 
        	data will be easily accessible via the *fit_neuron.data* package.  
        
        	
        Testing
        ------------
        There are two testing scripts that may be used.  Both scripts are 
        described in the documentation (http://pythonhosted.org/fit_neuron/).
        
        The first script is far simpler and easier to understand but is less configurable::
        
        	python -m fit_neuron.tests.test_model
        
        
        The more complicated and configurable testing script for fit_neuron can be run as follows:: 
        
        	python -m fit_neuron.tests.test
        
        
        This will create a directory called *test_output_figures* in the current directory.  
        
        Feel free to contact me at nicodjimenez [at] gmail.com if you have any questions / comments.  
        
        References
        ------------------
        
        .. [RB2005] Brette, Romain, and Wulfram Gerstner. "Adaptive exponential integrate-and-fire model as an effective description of neuronal activity." 
        			Journal of neurophysiology 94.5 (2005): 3637-3642.
        			
        .. [MN2009] Mihalas, Stefan, and Ernst Niebur. "A generalized linear integrate-and-fire neural model produces diverse spiking behaviors." 
        			Neural computation 21.3 (2009): 704-718.
        			
        .. [MS2011] Mensi, Skander, et al. "Parameter extraction and classification of three cortical neuron types reveals two distinct adaptation mechanisms." 
        			Journal of neurophysiology 107.6 (2012): 1756-1775.
        
        .. [RJ2008] Jolivet, Renaud, et al. "A benchmark test for a quantitative assessment of simple neuron models." 
        			Journal of neuroscience methods 169.2 (2008): 417-424.
        			
        .. [SS2003] Schreiber, S., et al. "A new correlation-based measure of spike timing reliability." 
        			Neurocomputing 52 (2003): 925-931.
        			
        .. [VR2001] van Rossum, Mark CW. "A novel spike distance." 
        			Neural Computation 13.4 (2001): 751-763.
        			
        .. [DA2003] Aronov, Dmitriy. "Fast algorithm for the metric-space analysis 
        			of simultaneous responses of multiple single neurons." Journal 
        			of Neuroscience Methods 124.2 (2003): 175-179.
        
        
Keywords: neuron linear integrate and fire patch clamp fitting parameter estimation spike distance metrics
Platform: UNKNOWN
Classifier: Development Status :: 1 - Planning
Classifier: License :: Freely Distributable
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
