neural-networks

Spiking neural networks (SNNs) are artificial intelligence (AI) models inspired by how biological neurons communicate with each other. While biological neurons exchange information in the form of electrical impulses, SNNs rely on brief signals known as spikes. SNNs have proved promising for reducing power consumption, as developers can ensure they do not process information continuously, […]

Nature Communications, Published online: 23 June 2026; doi:10.1038/s41467-026-74002-2 Alternating Neural Integrators (ANI) offer a non-intrusive “reuse-and-correct” framework to upgrade legacy simulators by alternating prior model evolution with learned neural corrections, improving fidelity in complex systems and enabling interpretable, data-driven refinement.

Researchers from the University of Lübeck and TU Hamburg published a technical paper titled “Beyond Silicon: Materials, Mechanisms, and Methods for Physical Neural Computing.” Abstract: “Physical implementations of neural computation now extend far beyond silicon hardware, encompassing substrates such as memristive devices, photonic circuits, mechanical metamaterials, microfluidic networks, chemi…

Nature Communications, Published online: 03 June 2026; doi:10.1038/s41467-026-73983-4 Optical computing could accelerate AI, but struggles to scale optical neural networks for diverse tasks. Here, the authors demonstrate a scalable photonic mixture-of-experts chip that expands network capacity in width using parallel optical experts for multi-task processing.

Nature, Published online: 13 May 2026; doi:10.1038/d41586-026-01321-1 A simple machine-learning algorithm, inspired by honeybees’ visual memory and sense of direction, enables a flying robot to pinpoint its home location.

Nowadays, neural networks act as a synonym for artificial intelligence. Present neural network models, although remarkably powerful, are inefficient both in terms of data and energy. Several alternative forms of neural networks have been proposed to address some of these problems. Specifically, spiking neural networks are suitable for efficient hardware implementations. However, effective learnin…

Physics-informed neural networks (PINNs) have shown remarkable prospects in solving forward and inverse problems involving partial differential equations (PDEs). But they often stumble when collocation points are distributed unevenly, a common feature of real simulations in which complex regions need denser sampling than simpler ones.

A new technical paper, “Neural Computers,” was published by researchers at Meta AI and KAUST. Abstract “We propose a new frontier: Neural Computers (NCs) — an emerging machine form that unifies computation, memory, and I/O in a learned runtime state. Unlike conventional computers, which execute explicit programs, agents, which act over external execution environments, and... » read more The post …

What if you could teach a team of AI programs to behave like graduate students, puzzling through complex design problems and checking each other’s work? Engineers at Duke University have done exactly that, building a crew of AI agents that can tackle intricate physics challenges nearly as well as human experts. The system, described in ACS Photonics, represents a step toward automating specialize…

A neural network, or artificial neural network, is a type of computing architecture that is based on a model of how a human brain functions — hence the name “neural.” Neural networks are made up of a collection of processing units called “nodes.” These nodes pass data to each other, just like how in a […] The post AI Neural Network appeared first on Upcoming Engineer .

I was happy to see John Hopfield was awarded the Nobel Prize in Physics for his work on neural networks. The award is based on this paper from 1982 Neural networks and physical systems with emergent collective computational abilities One thing I find beautiful about the paper is how Hopfield drew on ideas about spin glasses (many competing interactions lead to many ground states and a complex ene…

A method, a function, a technology: which is which? The post Machine Learning, AI, Neural Networks: How Do They Differ from Each Other? first appeared on Serious Science .

Neural network categorizes ambient sounds, giving users the power to choose what to hear. The post AI-Powered Headphones Filter Only Unwanted Noise #ASA186 first appeared on Acoustics.org .

IOP Publishing (IOPP) has launched a new, multidisciplinary, open access (OA) journal devoted to the design, development and application of artificial neural networks and brain-inspired systems towards advancing scientific discovery and realising emerging new computing technologies. The scope and characteristics of Neuromorphic Computing and Engineering (NCE) have been designed in close consultat…

Computer scientist Erol Gelenbe on history of neural networks research, Lapicque equation and reinforcement learning The post Neural Networks first appeared on Serious Science .