Abstract

Introduction The prototypical use of "classical" connectionist models (including the multilayer perceptron (MLP), the Hopfield network and the Kohonen self-organizing map) concerns static data processing. These classical models are not well suited to working with data varying over time. In response to this, temporal connectionist models have appeared and constitute a continuously growing research field. The purpose of this chapter is to present the main aspects of this research area and to review the key connectionist architectures that have been designed for solving temporal problems. The following section presents the fundamentals of temporal processing with neural networks. Several temporal connectionist models are then detailed in section 3. As a matter of illustration, important applications are reviewed in the third section. The chapter concludes with the presentation of a promising future issue: the extension of temporal processing to even more complex structured data. 2 Fund

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