Fisher and Shannon information in finite neural populations (Yarrow et al. 2012)


Here we model populations of rate-coding neurons with bell-shaped tuning curves and multiplicative Gaussian noise. This Matlab code supports the calculation of information theoretic (mutual information, stimulus-specific information, stimulus-specific surprise) and Fisher-based measures (Fisher information, I_Fisher, SSI_Fisher) in these population models. The information theoretic measures are computed by Monte Carlo integration, which allows computationally-intensive decompositions of the mutual information to be computed for relatively large populations (hundreds of neurons).

Model Type: Connectionist Network

Region(s) or Organism(s): Unknown

Model Concept(s): Rate-coding model neurons

Simulation Environment: MATLAB

Implementer(s): Yarrow, Stuart [s.yarrow at ed.ac.uk]

References:

Yarrow S, Challis E, Seri├Ęs P. (2012). Fisher and Shannon information in finite neural populations. Neural computation. 24 [PubMed]


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