288 pp. per issue
6 x 9, illustrated
2014 Impact factor:

Neural Computation

December 1, 2002, Vol. 14, No. 12, Pages 2959-2980
(doi: 10.1162/089976602760805368)
© 2002 Massachusetts Institute of Technology
Locality of Global Stochastic Interaction in Directed Acyclic Networks
Article PDF (496.65 KB)

The hypothesis of invariant maximization of interaction (IMI) is formulated within the setting of random fields. According to this hypothesis, learning processes maximize the stochastic interaction of the neurons subject to constraints. We consider the extrinsic constraint in terms of a fixed input distribution on the periphery of the network. Our main intrinsic constraint is given by a directed acyclic network structure. First mathematical results about the strong relation of the local information flow and the global interaction are stated in order to investigate the possibility of controlling IMI optimization in a completely local way. Furthermore, we discuss some relations of this approach to the optimization according to Linsker's Infomax principle.