Titlebook: Connectionist Models of Learning, Development and Evolution; Proceedings of the S Robert M. French,Jacques P. Sougné Conference proceedings

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Connectionist Models of Learning, Development and Evolution978-1-4471-0281-6Series ISSN 1431-6854

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Low Density Parity Check Codes, stimulus dimensions are irrelevant to the classification task in hand. A procedure is suggested by which a localist model can learn prototype representations that foeus on the relevant dimensions only. These permit good generalization which would be lacking in a simple exemplar-based model.

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Stability of the excavation faced, in particular, has observed the following level of precision: when a neuron A frres, neuron B would frre 151ms later and neuron C would fire precisely 289ms after that—with aprecision across trials of 1 ms! Such long delays require dozens of combined transmission delays from the presynaptic neuro

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Vittorio Gremigni,Alessandra Fallenial blood flow. They are usually based on univariate statistical techniques [3], however, since cognitive functions result from interactions, a number of new concepts and multivariate tools have recently been developed [5, 7, 12]. A different approach is to perform Input Variable Selection (IVS), a t

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https://doi.org/10.1007/BFb0019704 when the data is scaled by the time interval. This is called the scalar property of interval timing. Here a simple model of a neural clock is presented and shown to give rise to the scalar property. The model is an accumulator consisting of noisy, linear spiking neurons. It is analytically tractabl

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https://doi.org/10.1007/BFb0019704ion and learning. However, the types and levels of psychological modelling possible in artificial neural systems is limited by the current state of the technology. This chapter discusses modularity as illuminated from research in ., such as autonomous robots or virtual reality characters. We describ

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