Memristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity.

ABSTRACT: Brain-inspired computation can revolutionize information technology by introducing machines capable of recognizing patterns (images, speech, video) and interacting with the external world in a cognitive, humanlike way. Achieving this goal requires first to gain a detailed understanding of the brain operation, and second to identify a scalable microelectronic technology capable of reproducing some of the inherent functions of the human brain, such as the high synaptic connectivity (~10⁴) and the peculiar time-dependent synaptic plasticity. Here we demonstrate unsupervised learning and tracking in a spiking neural network with memristive synapses, where synaptic weights are updated via brain-inspired spike timing dependent plasticity (STDP). The synaptic conductance is updated by

SUBMITTER: Pedretti G 

PROVIDER: S-EPMC5509735 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature