lnpy is a Python module for fitting sensory neuron's receptive field parameters using a linear-nonlinear model. This module is build on top of several excellent Python modules, e.g., SciPy, NumPy, matplotlib, scikits-learn, statsmodels, and neo. The critical parts are written in C(++) and wrapped using Cython.

The main focus is on auditory data and there are several submodules that facilitate reading and converting data. However, the estimators may be just as well applied to data from different modalities, e.g., visual or somatosensory system.

• Spike-triggered average (STA, [1])
• Normalized reverse correlation (NRC, [2])
• Ridge regression [3]
• Generalized linear model (GLM, [4,5])
• Maximum informative dimensions (MID, [6]); requires external software
• Classification-based receptive field estimation (CbRF, [7])
• Time-varying RF estimation using non-zero mean priors [8]

* Stochastic gradient descent-based version version of some of the above algorithms [9]

Moreover, the toolbox has been designed such that most models can be used with a number of different priors, e.g., Gaussian and Laplace priors, and also mixtures of these priors.

lnpy works under Python 2.7 (Python 2.6 and Python 3.x have not been tested) and requires a working C/C++ compiler.

The required dependencies to build the package are

• NumPy >= 1.6.2
• SciPy >= 0.12
• cython
• matplotlib >= 1.2.1
• statsmodels >= 0.4.2
• neo >= 0.3
• scikit-learn >= 0.14
• sphinx
• sphinxcontrib-napoleon

1. Install the latest version of Python 2.7

2. Install dependencies, e.g.,

   pip install numpy scipy matplotlib neo

3. Install package by changing into the package directory and typing

   python setup.py install

   If you just want to compile the code in the package directory use

   python setup.py install build_ext -i

1. Install the latest version of WinPython 2.7.x.x (64 bit version)

   http://sourceforge.net/projects/winpython/

   I haven't tested Python(x,y) but it should also work.

2. Install the Microsoft Visual C++ 2008 Express edition

   http://download.microsoft.com/download/A/5/4/A54BADB6-9C3F-478D-8657-93B3FC9FE62D/vcsetup.exe

3. Install Microsoft Windows SDK for Windows 7 and .NET Framework 3.5 SP1

   http://www.microsoft.com/en-us/download/details.aspx?id=3138

   Note: this gives you the 64 bit compiler. However, you don't have to install all components. Just uncheck everything except "Developer Tools >> Visual C++ Compilers" (and the header files).

4. To install the package, open the WinPython command prompt ("WinPython Command Prompt.exe" in WinPython directory) and type

   "XXX:\Program Files (x86)\Microsoft Visual Studio 9.0\Common7\Tools\vsvars64.bat"

   where "XXX" denotes your system's root directory (probably "C")

5. Install dependencies; open the WinPython command prompt ("WinPython Command Prompt.exe" in WinPython directory) and type

   pip install neo

6. Install package by changing into the package directory and typing

   python setup.py install

That's all!

• Anaconda Python (includes many scientific packages; free version available)

  http://docs.continuum.io/anaconda/index.html

• Matlab-like IDE: Spyder (included in Anaconda and WinPython)

  https://pythonhosted.org/spyder/

• Scientific programming in Python:

  http://www.scipy.org/

• Switching from Matlab to Python (using numpy package):

  http://wiki.scipy.org/NumPy_for_Matlab_Users http://mathesaurus.sourceforge.net/matlab-numpy.html

• Plotting in Python:

  http://matplotlib.org/

• The trust region conjugate gradient descent algorithm as described in [8] has been adapted from the liblinear library available at:

  http://www.csie.ntu.edu.tw/~cjlin/liblinear/

  Copyright (c) 2007-2013 The LIBLINEAR Project. All rights reserved.

• The MID algorithm requires additional code available at

  https://github.com/sharpee/mid

• The gammatone filterbank is based on Volker Hohmann's implementation (cf. [9]) available at

  http://www.uni-oldenburg.de/medizin/departments/mediphysik-akustik/mediphysik/downloads/

  You have to download the code and unpack Gfb_analyze.h and Gfb_analyze.h to lnpy/transform/code/gammatone.

[1] deBoer E and Kuyper P: Triggered Correlation. IEEE Transactions on Biomedical Engineering, BM15, 169-179, 1968.

[2] Theunissen FE, Sen K, and Doupe AJ: Spectral-temporal receptive fields of nonlinear auditory neurons obtained using natural sounds. J Neurosci, 20, 2315-2331, 2000.

[3] Machens CK, Wehr MS, and Zador AM: Linearity of cortical receptive fields measured with natural sounds. J Neurosci, 24, 1089-1100, 2004.

[4] Paninski L: Maximum likelihood estimation of cascade point-process neural encoding models. Network, 15, 243-262, 2004.

[5] Truccolo W, Eden UT, Fellows MR, Donoghue JP, and Brown EN: A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects. J Neurophysiol, 93, 1074-1089, 2005.

[6] Sharpee T, Rust NC,and Bialek W: Analyzing neural responses to natural signals: maximally informative dimensions. Neural Comput, 16, 223-250, 2004.

[7] Meyer AF, Diepenbrock JP, Happel MF, Ohl FW, and Anemüller J: Discriminative Learning of Receptive Fields from Responses to Non-Gaussian Stimulus Ensembles. PLOS ONE, 9, e93062, 2014.

[8] Meyer AF, Diepenbrock JP, Happel MF, Ohl FW, and Anemüller J: Temporal variability of spectro-temporal receptive fields in the anesthetized auditory cortex. Front Comput Neurosci, 8, 165, 2014.

[9] Meyer AF, Diepenbrock JP, Happel MF, Ohl FW, and Anemüller J: Fast and robust estimation of spectro-temporal receptive fields using stochastic approximations. J Neurosci Methods, 246, 119-133, 2015.

[10] Lin CJ, Weng RC, and Keerthi SS: Trust Region Newton Method for Logistic Regression J. Mach. Learn. Res., 9, 627-650, 2008.

[11] Hohmann V: Frequency analysis and synthesis using a Gammatone filterbank ACTA ACUSTICA UNITED WITH ACUSTICA, 88, 433-442, 2002.