Integration of Biologically Inspired Temporal Mechanisms into a Cortical Framework for Sequence Processing
| Autor: | Frédéric Alexandre, Nicolas Rougier, Hervé Frezza-Buet |
|---|---|
| Přispěvatelé: | Neuromimetic intelligence (CORTEX), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP), none, Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2000 |
| Předmět: |
Dynamic time warping
traitement de séquences Computer science media_common.quotation_subject [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH] connexionism 02 engineering and technology temporal mechanisms 03 medical and health sciences 0302 clinical medicine Connectionism sequence processing Perception Learning rule 0202 electrical engineering electronic engineering information engineering Pattern matching Dimension (data warehouse) media_common Data processing business.industry Task (computing) cortex connectionnisme 020201 artificial intelligence & image processing Artificial intelligence business mécanismes temporels 030217 neurology & neurosurgery |
| Zdroj: | Neural, Symbolic and Reinforcement Methods for Sequence Learning Neural, Symbolic and Reinforcement Methods for Sequence Learning, Springer, 28 p, 2000 Sequence Learning ISBN: 9783540415978 Sequence Learning HAL |
| Popis: | Contribution à un ouvrage.; Whereas classical connectionist models can hardly cope with difficult dynamic tasks with a strong temporal factor, many temporal mechanisms inspired with neurobiological data has been proposed in the past and yield efficient time processing properties. The goal of this chapter is to show that, beyond these isolated mechanisms, their integration in a more general architectural and functional framework can potentiate their power and make them usable for non trivial behavioral tasks. We propose a cerebral framework, from the neuronal to the behavioral level, and give some applicative illustrations that underline the encouraging results obtained today. |
| Databáze: | OpenAIRE |
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