Memristive devices present a new device technology allowing for the realization of compact
non-volatile memories. Some of them are already in the process of industrialization …

In order to understand how the mammalian neocortex is performing computations, two
things are necessary; we need to have a good understanding of the available neuronal …

Brain-inspired neuromorphic computing is expected to revolutionize the architecture of
conventional digital computers and lead to a new generation of powerful computing …

Memristive nanodevices can feature a compact multilevel nonvolatile memory function, but
are prone to device variability. We propose a novel neural network-based computing …

We introduce a novel energy-efficient methodology “2-PCM Synapse” to use phase-change
memory (PCM) as synapses in large-scale neuromorphic systems. Our spiking neural …

A biologically inspired approach to learning temporally correlated patterns from a spiking
silicon retina is presented. Spikes are generated from the retina in response to relative …

Cognitive tasks are essential for the modern applications of electronics, and rely on the
capability to perform inference. The Von Neumann bottleneck is an important issue for such …

In this work, we demonstrate how phase change memory (PCM) devices can be used to
emulate biologically inspired synaptic functions in particular, potentiation and depression …

Hardware-based spiking neural networks (SNNs) inspired by a biological nervous system
are regarded as an innovative computing system with very low power consumption and …

Neural networks implemented in memristor-based hardware can provide fast and efficient in-
memory computation, but traditional learning methods such as error back-propagation are …