The memristor has been extensively used to facilitate the synaptic online learning of brain-
inspired spiking neural networks (SNNs). However, the current memristor-based work can …

Neural networks that can capture key principles underlying brain computation offer exciting
new opportunities for developing artificial intelligence and brain-like computing algorithms …

Evaluating the computational accuracy of Spiking Neural Network (SNN) implemented as in-
situ learning on large-scale memristor crossbars remains a challenge due to the lack of a …

We present a CMOS-Memristor hybrid analog design of a neuromorphic crossbar array with
integrated inference and training. Each crosspoint on the crossbar includes a memristor to …

Spiking Neural Networks (SNNs) have suitable properties for realizing efficient processing at
the edge, while having several similarities to brain-inspired computing that can be applied in …

This paper studies the use of Metropolis-Hastings sampling for training Spiking Neural
Network (SNN) hardware subject to strong unknown non-idealities, and compares the …

Optoelectronic memristors are suitable candidates for hardware implementation of optical
synapses in spiking neural networks (SNNs), thanks to their electrical and optical …

Spiking neural network is a promising endeavor to fulfill brain-like intelligence on the chip.
Its learning rule, ie, spike-timing-dependent plasticity (STDP), derived from neurobiology, is …