Abstract
Catalytic networks are abstracted from chemistry, and have recently been used to study cooperation in molecular evolution. Here catalytic networks are regarded as a connectionist model with sigma-pi units in a recurrent dynamics. This paper partly presents previous work on the learning and generalisation in an association task. A particular type of architecture for catalytic networks is put forward here. The architecture of doubly-linked chains has been found to provide useful dynamics for association, while it allows for a range of different chain lengths. The chains can also branch, giving for example a tree structure. The nodes at the branching points perform logical AND or OR, depending on their connection parameters. An example is demonstrated where words are associated with letters, and each letter can have alternative (localist) representations. The main features of this model are the high sensitivity to single inputs and the two-way association. Possible extensions to using stochastic dynamics and distributed representations are discussed.
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Hüning, H. (2001). Borrowing Dynamics from Evolution: Association using Catalytic Network Models. In: French, R.M., Sougné, J.P. (eds) Connectionist Models of Learning, Development and Evolution. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0281-6_24
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DOI: https://doi.org/10.1007/978-1-4471-0281-6_24
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