Computing with oscillators from theoretical underpinnings to applications and demonstrators.

Networks of coupled oscillators have far-reaching implications across various fields, providing insights into a plethora of dynamics. This review offers an in-depth overview of computing with oscillators covering computational capability, synchronization occurrence and mathematical formalism. We discuss numerous circuit design implementations, technology choices and applications from pattern retrieval, combinatorial optimization problems to machine learning algorithms.

From statistics to stories: understanding the complex landscape of missed medical appointments. A mixed-methods pilot study.

Background: Research suggests that in both France and the UK, between 5% and 10% of appointments with GPs are unattended. A comprehensive Irish study linked missed appointments with an increased short-term risk of mortality, prompting further investigation into the reasons behind absenteeism.

Aim: To delve into the underlying causes of missed appointments, within the context of an urban health centre.

A CMOS-compatible oscillation-based VO Ising machine solver.

Phase-encoded oscillating neural networks offer compelling advantages over metal-oxide-semiconductor-based technology for tackling complex optimization problems, with promising potential for ultralow power consumption and exceptionally rapid computational performance. In this work, we investigate the ability of these networks to solve optimization problems belonging to the nondeterministic polynomial time complexity class using nanoscale vanadium-dioxide-based oscillators integrated onto a Silicon platform. Specifically, we demonstrate how the dynamic behavior of coupled vanadium dioxide devices can effectively solve combinatorial optimization problems, including Graph Coloring, Max-cut, and Max-3SAT problems.

Portrait of intense communications within microfluidic neural networks.

In vitro model networks could provide cellular models of physiological relevance to reproduce and investigate the basic function of neural circuits on a chip in the laboratory. Several tools and methods have been developed since the past decade to build neural networks on a chip; among them, microfluidic circuits appear to be a highly promising approach. One of the numerous advantages of this approach is that it preserves stable somatic and axonal compartments over time due to physical barriers that prevent the soma from exploring undesired areas and guide neurites along defined pathways.

Energy-Performance Assessment of Oscillatory Neural Networks Based on VO Devices for Future Edge AI Computing.

Oscillatory neural network (ONN) is an emerging neuromorphic architecture composed of oscillators that implement neurons and are coupled by synapses. ONNs exhibit rich dynamics and associative properties, which can be used to solve problems in the analog domain according to the paradigm let physics compute. For example, compact oscillators made of VO2 material are good candidates for building low-power ONN architectures dedicated to AI applications at the edge, like pattern recognition.