Tarek Behi

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The Self-organizing Maps, proposed by Kohonen, have been used with a great deal of success in many applications. However, the basic SOM is designed to map patterns, or feature vectors, from an input space into an output space and does not take time into account. Speech recognition is a sequence of acoustic information and can't be considered as a static(More)
Synaptic plasticity seems to be a capital aspect of the dynamics of neural networks. It is about the physiological modifications of the synapse, which have like consequence a variation of the value of the synaptic weight. The information encoding is based on the precise timing of single spike events that is based on the relative timing of the preand(More)
Speech recognition has gradually improved over the years, phoneme recognition in particular. Phoneme recognition plays very important role in speech processing. Phoneme strings are basic representation for automatic language recognition and it is proved that language recognition results are highly correlated with phoneme recognition results. Nowadays, many(More)
In this paper, we propose new variants of unsupervised and competitive learning algorithms designed to deal with temporal sequences. These algorithms combine features from Spiking Neural Networks (SNNs) and the advantages of the hierarchical self organizing map (HSOM). The first variant named Hierarchical Dynamic recurrent spiking self-organizing map(More)
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