[Neuronal network with modifiable synapses: decoding of composite sensory messages under unsupervised and permanent learning].
Afferent sensory fibers carry usually messages which combine several primitive entities. A model of neural structure under unsupervised and permanent learning is proposed, which can detect the primary independent entities mixed in the afferent message. The model is a network of neurons mutually interconnected by means of inhibitory modifiable synapses with efficacies locally controlled by a specific conjunction law of pre- and post-synaptic activities.
D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D009431:Neural Conduction; D009473:Neuronal Plasticity; D009475:Neurons, Afferent / Q000502:physiology*; D013569:Synapses / Q000502:physiology*
Réseau de neurones à synapses modifiables: décodage de messages sensoriels composites par apprentissage non supervisé et permanent.