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(Neural Computation. 2006;18:1511-1526.)
© 2006 The MIT Press

Note

Optimal Neuronal Tuning for Finite Stimulus Spaces

W. Michael Brown

wmbrown{at}sandia.gov Computational Biology, Sandia National Laboratories, Albuquerque, NM, 87123, U.S.A.

Alex Bäcker

alex{at}caltech.edu Computational Biology, Sandia National Laboratories, Albuquerque, NM, 87123, and Division of Biology, California Institute of Technology, Pasadena, CA 91125, U.S.A.

The efficiency of neuronal encoding in sensory and motor systems has been proposed as a first principle governing response properties within the central nervous system. We present a continuation of a theoretical study presented by Zhang and Sejnowski, where the influence of neuronal tuning properties on encoding accuracy is analyzed using information theory. When a finite stimulus space is considered, we show that the encoding accuracy improves with narrow tuning for one- and two-dimensional stimuli. For three dimensions and higher, there is an optimal tuning width.






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