Research in photonic computing has flourished due to the proliferation of optoelectronic
components on photonic integration platforms. Photonic integrated circuits have enabled …

The design of robots that interact autonomously with the environment and exhibit complex
behaviours is an open challenge that can benefit from understanding what makes living …

Deep artificial neural networks apply principles of the brain's information processing that led
to breakthroughs in machine learning spanning many problem domains. Neuromorphic …

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 …

Hand gestures are a form of non-verbal communication used by individuals in conjunction
with speech to communicate. Nowadays, with the increasing use of technology, hand …

The rapid growth of deep learning, spurred by its successes in various fields ranging from
face recognition [1] to game playing [2], has also triggered a growing interest in the design of …

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 …

Lifelong learning—an agent's ability to learn throughout its lifetime—is a hallmark of
biological learning systems and a central challenge for artificial intelligence (AI). The …

For a biological agent operating under environmental pressure, energy consumption and
reaction times are of critical importance. Similarly, engineered systems are optimized for …

To rapidly process temporal information at a low metabolic cost, biological neurons integrate
inputs as an analog sum, but communicate with spikes, binary events in time. Analog …