Adaptive Synchronization of Activities in a Recurrent Network

Thomas Voegtlin 1
1 CORTEX - Neuromimetic intelligence
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : Predictive learning rules, where synaptic changes are driven by the difference between a random input and its reconstruction derived from internal variables, have proven to be very stable and efficient. However, it is not clear how such learning rules could take place in biological synapses. Here we propose an implementation that exploits the synchronization of neural activities within a recurrent network. In this framework, the asymmetric shape of spike-timing-dependent plasticity (STDP) can be interpreted as a self-stabilizing mechanism. Our results suggest a novel hypothesis concerning the computational role of neural synchrony and oscillations.
Type de document :
Article dans une revue
Neural Computation, Massachusetts Institute of Technology Press (MIT Press), 2009, 21 (6), pp.1749-1775
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https://hal.inria.fr/inria-00338250
Contributeur : Thomas Voegtlin <>
Soumis le : mercredi 12 novembre 2008 - 14:08:53
Dernière modification le : jeudi 11 janvier 2018 - 06:19:48

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  • HAL Id : inria-00338250, version 1

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Thomas Voegtlin. Adaptive Synchronization of Activities in a Recurrent Network. Neural Computation, Massachusetts Institute of Technology Press (MIT Press), 2009, 21 (6), pp.1749-1775. 〈inria-00338250〉

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