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 …

As a new brain-inspired computational model of the artificial neural network, a spiking
neural network encodes and processes neural information through precisely timed spike …

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 …

A vast majority of computation in the brain is performed by spiking neural networks. Despite
the ubiquity of such spiking, we currently lack an understanding of how biological spiking …

Spiking neural networks are the basis of versatile and power-efficient information processing
in the brain. Although we currently lack a detailed understanding of how these networks …

Neural activity is often described in terms of population-level factors extracted from the
responses of many neurons. Factors provide a lower-dimensional description with the aim of …

Populations of neurons display an extraordinary diversity in the behaviors they affect and
display. Machine learning techniques have recently emerged that allow us to create …

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 …

Artificial neural networks (ANNs) have emerged as an essential tool in machine learning,
achieving remarkable success across diverse domains, including image and speech …