Abstract:
: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.
journal_name
Neural Computjournal_title
Neural computationauthors
Ay Ndoi
10.1162/089976602760805368subject
Has Abstractpub_date
2002-12-01 00:00:00pages
2959-80issue
12eissn
0899-7667issn
1530-888Xjournal_volume
14pub_type
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