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

The ever‐increasing demand for higher‐capacity digital memory shows no sign of declining.
The conventional strategy for meeting such demand, ie shrinking of the memory cell size …

Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices,
and models that contrast the pervasive von Neumann computer architecture. This …

Implementing compact, low-power artificial neural processing systems with real-time on-line
learning abilities is still an open challenge. In this paper we present a full-custom mixed …

Abstract The Hodgkin–Huxley model for action potential generation in biological axons is
central for understanding the computational capability of the nervous system and emulating …

The explosion of “big data” applications imposes severe challenges of speed and scalability
on traditional computer systems. As the performance of traditional Von Neumann machines …

Neuromorphic hardware strives to emulate brain-like neural networks and thus holds the
promise for scalable, low-power information processing on temporal data streams. Yet, to …

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 …

The ability to carry out signal processing, classification, recognition, and computation in
artificial spiking neural networks (SNNs) is mediated by their synapses. In particular, through …

Spiking neural networks (SNNs) attempt to emulate information processing in the
mammalian brain based on massively parallel arrays of neurons that communicate via spike …