By Steven Davis

During the last decade, an rising kind of computational modeling has gradually received the honour of many researchers as a greatly new and promising method of cognitive technological know-how. recognized by way of a few names, together with "connectionism," "neural networks," and "parallel allotted processing" (PDP), this technique of computation makes an attempt to version the neural approaches which are assumed to underlie cognitive services in humans. in contrast to the electronic computation tools utilized by AI researchers, connectionist versions declare to approximate the type of spontaneous, inventive, and just a little unpredictable habit of human brokers. even though, over the past few years, a heated controversy has arisen over the level to which connectionist types may be able to supply winning factors for better cognitive strategies. A significant subject of this ebook reports the adequacy of contemporary makes an attempt to enforce better cognitive approaches in connectionist networks. Cognitive scientists, cognitive psychologists, linguists, philosophers, laptop scientists, and others exploring this interesting technology will locate this ebook crucial analyzing.

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In this section we discuss these relationships between modularity and learning and we contrast Hinton and Becker's architecture with another recently proposed modular connectionist architecture. In situating the problem of modularity and learning in a larger context it is useful to distinguish between two kinds of computational processes: convergent processes and divergent processes. Convergent computation involves taking data in different channels or different formats and integrating them into a common channel or format.

Such replication of effort is the limiting case of convergent computation. It is also possible to conceive of learning algorithms for modular networks in which the emphasis is on divergent computation. Jacobs, Jordan, Nowlan, and Hinton (1991) describe a supervised learning algorithm for multi-network architectures in which the modules learn to compute different functions of a single input vector. The assumptions on which the architecture is based are different from Hinton and Becker's proposal, reflecting the differing nature of divergent and convergent computation.

2b it has mastered the echoes in the training set, it will generalize: it will reliably identify mine and rock echoes from outside its training set echoes it has never heard before. Mine echoes, it turns out, are indeed united by some subtle weave of features, to which weave the network has become tuned during the training process. The same is true for rock echoes. ) Here we have a binary discrimination between a pair of diffuse and very hard-to-define acoustic properties. Indeed, we never did define them!

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