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