Neuromorphic computing is a brain-inspired approach to hardware and algorithm design
that efficiently realizes artificial neural networks. Neuromorphic designers apply the …

Modern generative machine learning models are able to create realistic outputs far beyond
their training data, such as photorealistic artwork, accurate protein structures or …

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 …

The brain's connectivity is locally dense and globally sparse, forming a small-world graph—
a principle prevalent in the evolution of various species, suggesting a universal solution for …

Spiking neural networks (SNN), also often referred to as the third generation of neural
networks, carry the potential for a massive reduction in memory and energy consumption …

While Moore's law has driven exponential computing power expectations, its nearing end
calls for new avenues for improving the overall system performance. One of these avenues …

The term “neuromorphic” was originally introduced by Mead in the late 1980s, 1 referring to
devices and systems that imitated certain elements of biological neural systems. However …

Chaos and unpredictability are traditionally synonymous, yet large-scale machine-learning
methods recently have demonstrated a surprising ability to forecast chaotic systems well …

With the emergence of neuromorphic hardware as a promising low-power parallel
computing platform, the need for tools that allow researchers and engineers to efficiently …

The rise of mobility, IoT and wearables has shifted processing to the edge of the sensors,
driven by the need to reduce latency, communication costs and overall energy consumption …