Infomax is an optimization principle for artificial neural networks and other information processing systems. It prescribes that a function that maps a set of input values I to a set of output values O should be chosen or learned so as to maximize the average Shannon mutual information between I and O, subject to a set of specified constraints and/or noise processes. Infomax algorithms are learning algorithms that perform this optimization process. The principle was described by Linsker in 1988.
Infomax, in its zero-noise limit, is related to the principle of redundancy reduction proposed for biological sensory processing by Horace Barlow in 1961, and applied quantitatively to retinal processing by Atick and Redlich.
One of the applications of infomax has been to an independent component analysis algorithm that finds independent signals by maximizing entropy. Infomax-based ICA was described by Bell and Sejnowski, and Nadal and Parga in 1995. 
- Linsker R (1988). "Self-organization in a perceptual network" (PDF). IEEE Computer. 21 (3): 105–17. doi:10.1109/2.36.
- Barlow, H. (1961). "Possible principles underlying the transformations of sensory messages". In Rosenblith, W. Sensory Communication. Cambridge MA: MIT Press. pp. 217–234.
- Atick JJ, Redlich AN (1992). "What does the retina know about natural scenes?". Neural Computation. 4 (2): 196–210. doi:10.1162/neco.1922.214.171.124.
- Bell AJ, Sejnowski TJ (November 1995). "An information-maximization approach to blind separation and blind deconvolution". Neural Comput. 7 (6): 1129–59. doi:10.1162/neco.19126.96.36.1999. PMID 7584893.
- Nadal J.P., Parga N. (1999). "Sensory coding: information maximization and redundancy reduction". Neural information processing, G. Burdet, P. Combe and O. Parodi Eds., World Scientific Series in Mathematical Biology and Medecine. 7: 164–171.
- Bell AJ, Sejnowski TJ (December 1997). "The "Independent Components" of Natural Scenes are Edge Filters". Vision Res. 37 (23): 3327–38. doi:10.1016/S0042-6989(97)00121-1. PMC 2882863. PMID 9425547.
- Linsker R (1997). "A local learning rule that enables information maximization for arbitrary input distributions". Neural Computation. 9 (8): 1661–65. doi:10.1162/neco.19188.8.131.521.
- Stone, J. V. (2004). Independent Component Analysis: A tutorial introduction. Cambridge MA: MIT Press. ISBN 0-262-69315-1.
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