Y. Ma and R. F. DeMara, "Localized Self-Contained Adaptive Networks for Hybrid- Symbolic Reasoning," in Proceedings of the Fourth Joint Conference on Information Sciences (JCIS?бе98), pp. 81 - 86, Research Triangle Park, North Carolina, U.S.A., October 24 - 28, 1998. Abstract: Hybrid-Symbolic processing has been gaining interest over the past decade. This is due to the problems of symbolic representations, which are ambiguous, brittle, lack of parallelism. Sub-symbolic representations have problems of lacking variable binding, symbolic composition and decomposition, and structured representations. Integration of these two representations can mitigate each other's shortcomings. The proposed paradigm: Localized Self-Contained Adaptive Networks(LSCAN) is a localist network using AND and OR evaluators to represent relations between knowledge entities. For optimization of each sub-network, the LSCAN provides learning capabilities for both feed-forward and lateral relations between network nodes.