Spike-Timing-Dependent and Spike-Shape-Independent Plasticities with Dopamine-Like Modulation in Nanocomposite Memristive Synapses

摘要

In hardware neuromorphic systems (NSs), memristors are used as synaptic connections. In such systems, spike-timing-dependent plasticity (STDP) is a promising local learning rule. Herein, STDP is studied in a system composed of a pair of hardware or software neurons connected by a (CoFeB)_x(LiNbO_3)_(100-x) nanocomposite-based memristor. The dopamine-like modulation of memristorbased STDP is implemented simply by the change in the polarity of spikes generated by artificial neurons operating in the inhibitory or excitatory mode. This modulation method is shown to be compatible with hardware neurons, different spike shapes, and is used in spiking NSs with bioinspired dopamine-like reinforcement learning.