IFB (Identifying Functional Bases)

Supplemental Code for our Neural Computation paper “Identifying functional bases for multidimensional neural computations” can be found here:
https://github.com/sharpee/ifb

Download

To just grab the code, run: git clone git://github.com/sharpee/ifb.git

Or if you don’t want to use git: download it here

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Maximally Informative Dimensions

The technique of maximizing mutual information between the spike train and the stimulus is described in [1,2].

Our code has been migrated to github here: https://github.com/sharpee/. You will need git to download our code, but it’s free to download.

More documentation is available here: https://github.com/sharpee/mid.

Download

To just grab the code, run: git clone git://github.com/sharpee/mid.git

Or if you don’t want to use git: download it here

Matlab Version of MID: https://github.com/jkaardal/matlab_mid_wrapper/

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References

[1] T. Sharpee, N. Rust, and W. Bialek. “Maximally informative dimensions: Analyzing neural responses to natural signals.” in NIPS: 261-268, 2003.
[2] T. Sharpee, N. Rust, and W. Bialek. “Analyzing neural responses to natural signals: Maximally informative dimensions.” in Neural Comp.: 16, 223-250, 2004.
[3] T. Sharpee, H. Sugihara, A.V. Kurgansky, S. Rebrik, M.P. Stryker, and K.D. Miller. “Probing feature selectvity of neurons in primary visual cortex with natural stimuli”, Proc. of SPIE: 5467, pp. 212-222, 2004.
[4] T. Sharpee, “Comparion of information and variance optimization strategies for characterizing neural feature selectivity” in Statistics in Medicine.: 26, 4009-4031, 2007.
[5] M. Kouh and T. Sharpee, “Estimating linear-nonlinear models using Renyi divergences”, Network: Computationa in Neural Systems.: 20(2):49-68, 2009.