title: Associative learning in hierarchical self-organizing learning arrays.
abstract: In this paper, we introduce feedback-based associative learning in self-organized learning arrays (SOLAR). SOLAR structures are hierarchically organized networks of sparsely connected neurons that define their own functions and select their interconnections locally. This paper provides a description of neuron self-organization and signal processing. Feedforward processing is used to make necessary correlations and learn the input patterns. Discovered associations between neuron inputs are used to generate feedback signals. These feedback signals, when propagated to the primary inputs, can establish the expected input values. This can be used for heteroassociative (HA) and autoassociative (AA) learning and pattern recognition. Example applications in HA learning are given.
authors.affiliation: School of Electrical Engineering and Computer Science, Ohio University, Athens, OH 45701, USA. starzyk@bobcat.ent.ohiou.edu
authors.forename: Janusz A
authors.initials: JA
authors.lastname: Starzyk
authors.forename: Zhen
authors.initials: Z
authors.lastname: Zhu
authors.forename: Yue
authors.initials: Y
authors.lastname: Li
mesh_terms: D000465:Algorithms; D016247:Information Storage and Retrieval / Q000379:methods*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted