title: The effect of molecular inhibition on evolutionary learning: studies in the hypernetwork architecture.
abstract: The hypernetwork architecture is a biologically inspired learning model based on abstract molecules and molecular interactions that exhibits functional and organizational correlation with biological systems. Hypernetwork organisms were trained, by molecular evolution, to solve N-input parity tasks. We found that learning improves when molecules exhibit inhibitory sites, allowing molecular inhibition and opening the possibility of forming negative feedback regulatory pathways. Optimal learning is achieved when at least 20% of the molecules in each cell have inhibitory sites. Intra-cellular as well as inter-cellular molecular inhibitions play an important role in the information processing of hypernetwork organisms, by maintaining a balance of the molecular cascade reactions. Similar mechanisms inside neurons are considered important for memory.
authors.affiliation: Department of Computer Science, Biocomputing Laboratory, Wayne State University, 431 State Hall, Detroit, MI 48202, USA. jls@cs.wyane.edu
authors.forename: Jose L
authors.initials: JL
authors.lastname: Segovia-Juarez
authors.forename: Silvano
authors.initials: S
authors.lastname: Colombano
mesh_terms: D000465:Algorithms; D005075:Biological Evolution; D007858:Learning; D008954:Models, Biological