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A recent study published in the journal Artificial Intelligence in Geosciences introduced an advanced method for automatic microfossil detection and analysis. The research team consisted of members from the machine learning group at the University of Tromso (UiT) The Arctic University of Norway.

They have developed a pipeline for extracting fossil information from microscope slide images. They found that deep learning techniques outperform traditional image processing methods and that self-supervision can be effectively used for feature extraction.

Kinetic Automation utilizes computer vision and machine-learning software for diagnosing and recalibrating advanced vehicle systems.

First-of-its-kind brand video premieres during cannes lions festival.

Toys R Us brand is leaping ahead of the curve by creating the first-ever brand film using OpenAI’s new text-to-video tool, Sora.

Continue reading “Toys R Us unveils first commercial made with OpenAI’s Sora” »

Ups now we have unsupported countries…by what other parameter will soon someone else be pronounced unsupported?

It’s not entirely clear what actions the ChatGPT maker plans to take, if any.

Scientists are watching with concern as India prepares to break ground on a long-delayed plan to connect the country’s rivers.

A parasitic fungus compels its insect host to behave in strange ways by hijacking secretory neurons and circadian pathways.

Inspired by the principles of the biological nervous system, neuromorphic engineering has brought a promising alternative approach to intelligence computing with high energy efficiency and low consumption. As pivotal components of neuromorphic system, artificial spiking neurons are powerful information processing units and can achieve highly complex nonlinear computations. By leveraging the switching dynamic characteristics of memristive device, memristive neurons show rich spiking behaviors with simple circuit. This report reviews the memristive neurons and their applications in neuromorphic sensing and computing systems. The switching mechanisms that endow memristive devices with rich dynamics and nonlinearity are highlighted, and subsequently various nonlinear spiking neuron behaviors emulated in these memristive devices are reviewed. Then, recent development is introduced on neuromorphic system with memristive neurons for sensing and computing. Finally, we discuss challenges and outlooks of the memristive neurons toward high-performance neuromorphic hardware systems and provide an insightful perspective for the development of interactive neuromorphic electronic systems.

Keywords: Memristive devices; artificial neurons; neuromorphic computing; neuromorphic sensing; spiking dynamics.

© 2023 The Author(s). Published by National Institute for Materials Science in partnership with Taylor & Francis Group.

Growth of data eases the way to access the world but requires increasing amounts of energy to store and process. Neuromorphic electronics has emerged in the last decade, inspired by biological neurons and synapses, with in-memory computing ability, extenuating the ‘von Neumann bottleneck’ between the memory and processor and offering a promising solution to reduce the efforts both in data storage and processing, thanks to their multi-bit non-volatility, biology-emulated characteristics, and silicon compatibility. This work reviews the recent advances in emerging memristive devices for artificial neuron and synapse applications, including memory and data-processing ability: the physics and characteristics are discussed first, i.e., valence changing, electrochemical metallization, phase changing, interfaced-controlling, charge-trapping, ferroelectric tunnelling, and spin-transfer torquing. Next, we propose a universal benchmark for the artificial synapse and neuron devices on spiking energy consumption, standby power consumption, and spike timing. Based on the benchmark, we address the challenges, suggest the guidelines for intra-device and inter-device design, and provide an outlook for the neuromorphic applications of resistive switching-based artificial neuron and synapse devices.

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Neuromorphic computing holds promise for building next-generation intelligent systems in a more energy-efficient way than the conventional von Neumann computing architecture. Memristive hardware, which mimics biological neurons and synapses, offers high-speed operation and low power consumption, enabling energy-and area-efficient, brain-inspired computing. Here, recent advances in memristive materials and strategies that emulate synaptic functions for neuromorphic computing are highlighted. The working principles and characteristics of biological neurons and synapses, which can be mimicked by memristive devices, are presented. Besides device structures and operation with different external stimuli such as electric, magnetic, and optical fields, how memristive materials with a rich variety of underlying physical mechanisms can allow fast, reliable, and low-power neuromorphic applications is also discussed. Finally, device requirements are examined and a perspective on challenges in developing memristive materials for device engineering and computing science is given.

Keywords: artificial synapses; memristive materials; neurons; synaptic plasticity.

© 2021 Wiley-VCH GmbH.