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Much of the progress in the fields constituting cognitive science has been based upon the use of explicit information processing models, almost exclusively patterned after conventional serial computers. An extension of these ideas to massively parallel, connectianist models appears to offer a number of advantages. After a preliminary discussion, this paper introduces a general connectionist model and considers how it might be used in cognitive science. Among the issues addressed are: stability and noise-sensitivity, distributed decisionmaking, time and sequence problems, and the representation of complex concepts.

SUMMARYThis paper presents a general method for dynamic particle refinement in smoothed particle hydrodynamics (SPH). Candidate particles are split into several 'daughter' particles according to a given refinement pattern centred about the original particle. Through the solution of a non-linear minimization problem the optimal mass distribution of the daughter particles is obtained so as to reduce the errors introduced to the underlying density field. This procedure necessarily conserves the mass of the system. Conservation of energy and momentum results are also discussed.

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