Abstract
Time is an important dimension in many real-world problems. This is particularly true for behavioral tasks where the temporal factor is critical. Consider for example the analysis of a perceptual scene or the organization of behavior in a planning task. Temporal problems are often solved using temporal techniques like Markovian Models or Dynamic Time Warping. Classical connectionist models are powerful for pattern matching tasks but exhibit some weaknesses in dealing with dynamic tasks involving the temporal dimension. Thus, they are efficient for off-line statistical data processing, but must be adapted for situated tasks which are intrinsically temporal.
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Frezza-Buet, H., Rougier, N., Alexandre, F. (2000). Integration of Biologically Inspired Temporal Mechanisms into a Cortical Framework for Sequence Processing. In: Sun, R., Giles, C.L. (eds) Sequence Learning. Lecture Notes in Computer Science(), vol 1828. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44565-X_15
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DOI: https://doi.org/10.1007/3-540-44565-X_15
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