Information-theoretic analysis of signal processing systems: application to neural coding

Author

Johnson, Don H. ; Gruner, Charlotte M.

Author_Institution

Dept. of Electr. & Comput. Eng., Rice Univ., Houston, TX, USA

fYear

1998

fDate

16-21 Aug 1998

Firstpage

22

Abstract

We analyze the signal processing capabilities of a system given access to its inputs and outputs, but not assuming any special structure other than they are stochastic. The analysis techniques are based on information-theoretic distance measures and on empirical theories derived from work in universal signal processing. We apply our techniques to the analysis of single- and multi-neuron discharge patterns, finding that neurons can encode multiple attributes simultaneously and in a time-varying fashion

Keywords

information theory; neural nets; signal processing; stochastic processes; distance measures; information-theoretic analysis; multi-neuron discharge patterns; neural coding; signal processing systems; single-neuron discharge patterns; stochastic inputs; stochastic outputs; Application software; Biology computing; Biomedical signal processing; Computational biology; Information analysis; Information technology; Neurons; Parameter estimation; Signal analysis; Signal processing;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Theory, 1998. Proceedings. 1998 IEEE International Symposium on

Conference_Location

Cambridge, MA

Print_ISBN

0-7803-5000-6

Type

conf

DOI

10.1109/ISIT.1998.708601

Filename

708601