DocumentCode :
1928718
Title :
Almost all noise types can improve the mutual information of threshold neurons that detect subthreshold signals
Author :
Kosko, Bart ; Mitaim, Sanya
Author_Institution :
Signal & Image Process. Inst., Univ. of Southern California, Los Angeles, CA, USA
Volume :
4
fYear :
2003
fDate :
20-24 July 2003
Firstpage :
2740
Abstract :
Two new theorems show that small amounts of noise can increase the mutual information of threshold neurons that detect subthreshold signals. The first theorem shows that this "stochastic resonance" effect holds for all finite-variance noise probability density functions that obey a simple mean constraint that the user can control. The second theorem shows that this effect holds for all infinite-variance noise types in the broad class of stable distributions. Stable bell curves can model extremely impulsive noise environments. So the second theorem shows that this stochastic-resonance effect is robust against violent fluctuations in the additive noise process.
Keywords :
information theory; neural nets; noise; probability; signal detection; additive noise process; finite-variance noise probability density functions; impulsive noise environments; infinite-variance noise types; mutual information; stable bell curves; stochastic resonance; stochastic resonance effect; subthreshold signal detection; threshold neurons; Additive noise; Constraint theory; Fluctuations; Mutual information; Neurons; Noise robustness; Probability density function; Signal detection; Stochastic resonance; Working environment noise;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2003. Proceedings of the International Joint Conference on
ISSN :
1098-7576
Print_ISBN :
0-7803-7898-9
Type :
conf
DOI :
10.1109/IJCNN.2003.1224001
Filename :
1224001
Link To Document :
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