A Local Learning Rule That Enables Information Maximization for Arbitrary Input Distributions

Neural Computation

Volumn 9, Issue 8, 1997, Pages 1661-1665

  Linsker, Ralph ^a  

^a IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0000578525     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/neco.1997.9.8.1661     Document Type: Article

References (7)

1. Amari, S., Cichocki, A., & Yang, H. H. (1996). A new learning algorithm for blind signal separation. In D. S. Touretzky, M. C. Mozer, & M. E. Hasselmo (Eds.), Advances in neural information processing systems, 8 (pp. 757-763). Cambridge, MA: MIT Press.
2. Atick, J. J. (1992). Could information theory provide an ecological theory of sensory processing? Network, 3, 213-251.
3. Bell, A. J., & Sejnowski, T. J. (1995). An information-maximisation approach to blind separation and blind deconvolution. Neural Comp., 7, 1129-1159.
4. Linsker, R. (1988). Self-organization in a perceptual network. Computer, 21 March, 105-117.
5. Linsker, R. (1989a). An application of the principle of maximum information preservation to linear systems. In D. S. Touretzky (Ed.), Advances in neural information processing systems, 1 (pp. 186-194). San Mateo, CA: Morgan Kaufmann.
6. Linsker, R. (1989b). How to generate ordered maps by maximizing the mutual information between input and output signals. Neural Comp., 1, 402-411.
7. Linsker, R. (1992). Local synaptic learning rules suffice to maximize mutual information in a linear network. Neural Comp., 4, 691-702.