Memristor-based synaptic plasticity and unsupervised learning of spiking neural networks

Synaptic plasticity is studied herein using a voltage-driven memristor model. The bidirectional weight update technique is demonstrated, and significant synaptic features, including nonlinear and threshold-based learning and long-term potentiation and long-term depression, are emulated. The spike-timing-dependent plasticity (STDP) learning characteristic curve is obtained from exhaustive simulations. Then, using leaky integrate and fire neurons and memristive synapses, fully connected spiking neural networks with 2 x 2 and 4 x 2 architectures are constructed, and unsupervised learning using the STDP rule and winner-takes-all strategy is evaluated in those networks for pattern classification.