Neuromorphic computing technologies will be important for the future of computing, but
much of the work in neuromorphic computing has focused on hardware development. Here …

Predictive coding offers a potentially unifying account of cortical function--postulating that the
core function of the brain is to minimize prediction errors with respect to a generative model …

The brain is the perfect place to look for inspiration to develop more efficient neural
networks. The inner workings of our synapses and neurons provide a glimpse at what the …

Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic
chips with high energy efficiency by introducing neural dynamics and spike properties. As …

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 …

Abstract Spiking Neural Networks (SNNs) have attracted enormous research interest due to
temporal information processing capability, low power consumption, and high biological …

We consider two biologically plausible structures, the Spiking Neural Network (SNN) and the
self-attention mechanism. The former offers an energy-efficient and event-driven paradigm …

Spiking neural networks (SNNs) are nature's versatile solution to fault-tolerant, energy-
efficient signal processing. To translate these benefits into hardware, a growing number of …

Recurrently connected networks of spiking neurons underlie the astounding information
processing capabilities of the brain. Yet in spite of extensive research, how they can learn …

Neuromorphic Computing, a concept pioneered in the late 1980s, is receiving a lot of
attention lately due to its promise of reducing the computational energy, latency, as well as …