Spiking neural networks (SNNs) are inspired by information processing in biology, where
sparse and asynchronous binary signals are communicated and processed in a massively …

Dense crossbar arrays of non-volatile memory (NVM) devices represent one possible path
for implementing massively-parallel and highly energy-efficient neuromorphic computing …

Modern computation based on von Neumann architecture is now a mature cutting-edge
science. In the von Neumann architecture, processing and memory units are implemented …

Photonic systems for high-performance information processing have attracted renewed
interest. Neuromorphic silicon photonics has the potential to integrate processing functions …

Memristors are memory resistors that promise the efficient implementation of synaptic
weights in artificial neural networks. Whereas demonstrations of the synaptic operation of …

Understanding how biological neural networks carry out learning using spike-based local
plasticity mechanisms can lead to the development of real-time, energy-efficient, and …

In this study, we present a highly configurable neuromorphic computing substrate and use it
for emulating several types of neural networks. At the heart of this system lies a mixed-signal …

Microring weight banks present novel opportunities for reconfigurable, high-performance
analog signal processing in photonics. Controlling microring filter response is a challenge …

Recent studies have shown that synaptic unreliability is a robust and sufficient mechanism
for inducing the stochasticity observed in cortex. Here, we introduce Synaptic Sampling …

We present results from a new approach to learning and plasticity in neuromorphic
hardware systems: to enable flexibility in implementable learning mechanisms while …