The term connectionism is usually applied to neural networks. There are, however, many other models that are mathematically similar, including classifier systems, immune networks, autocatalytic chemical reaction networks, and others. In view of this similarity, it is appropriate to broaden the term connectionism. I define a connectionist model as a dynamical system with two properties: (1) The interactions between the variables at any given time are explicitly constrained to a finite list of connections. (2) The connections are fluid, in that their strength and/or pattern of connectivity can change with time.

This paper reviews the four examples listed above and maps them into a common mathematical framework, discussing their similarities and differences. It also suggests new applications of connectionist models, and poses some problems to be addressed in an eventual theory of connectionist systems.


Farmer, J.D. (1990). 'A Rosetta Stone for Connectionism.' Physica D, 42, pp.153-187.
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