Self-Adaptive Spike-Time-Dependent Plasticity of Metal-Oxide Memristors.

ABSTRACT: Metal-oxide memristors have emerged as promising candidates for hardware implementation of artificial synapses - the key components of high-performance, analog neuromorphic networks - due to their excellent scaling prospects. Since some advanced cognitive tasks require spiking neuromorphic networks, which explicitly model individual neural pulses ("spikes") in biological neural systems, it is crucial for memristive synapses to support the spike-time-dependent plasticity (STDP). A major challenge for the STDP implementation is that, in contrast to some simplistic models of the plasticity, the elementary change of a synaptic weight in an artificial hardware synapse depends not only on the pre-synaptic and post-synaptic signals, but also on the initial weight (memristor's conductance) value.

SUBMITTER: Prezioso M 

PROVIDER: S-EPMC4759564 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature