Real time unsupervised learning of visual stimuli in neuromorphic VLSI systems.

ABSTRACT: Neuromorphic chips embody computational principles operating in the nervous system, into microelectronic devices. In this domain it is important to identify computational primitives that theory and experiments suggest as generic and reusable cognitive elements. One such element is provided by attractor dynamics in recurrent networks. Point attractors are equilibrium states of the dynamics (up to fluctuations), determined by the synaptic structure of the network; a 'basin' of attraction comprises all initial states leading to a given attractor upon relaxation, hence making attractor dynamics suitable to implement robust associative memory. The initial network state is dictated by the stimulus, and relaxation to the attractor state implements the retrieval of the corresponding memorized prot

SUBMITTER: Giulioni M 

PROVIDER: S-EPMC4604465 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature