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Category: computing – Page 148

This study introduces a novel methodology for consciousness science. Consciousness as we understand it pretheoretically is inherently subjective, yet the data available to science are irreducibly intersubjective. This poses a unique challenge for attempts to investigate consciousness empirically. We meet this challenge by combining two insights. First, we emphasize the role that computational models play in integrating results relevant to consciousness from across the cognitive sciences. This move echoes Alan Newell’s call that the language and concepts of computer science serve as a lingua franca for integrative cognitive science. Second, our central contribution is a new method for validating computational models that treats them as providing negative data on consciousness: data about what consciousness is not. This method is designed to support a quantitative science of consciousness while avoiding metaphysical commitments. We discuss how this methodology applies to current and future research and address questions that others have raised.

Keywords: computationalism; consciousness; evidence; functionalism; methodology; modeling.

© The Author(s) 2021. Published by Oxford University Press.

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Quantum computers have the potential to be revolutionary tools for their ability to perform calculations that would take classical computers many years to resolve.

But to make an effective quantum computer, you need a reliable quantum bit, or , that can exist in a simultaneous 0 or 1 state for a sufficiently long period, known as its coherence time.

One promising approach is trapping a on a solid surface, called an electron-on-solid-neon qubit. A study led by FAMU-FSU College of Engineering Professor Wei Guo that was published in Physical Review Letters shows new insight into the that describes the condition of electrons on such a qubit, information that can help engineers build this innovative technology.

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As lasers go, those made of Titanium-sapphire (Ti: sapphire) are considered to have “unmatched” performance. They are indispensable in many fields, including cutting-edge quantum optics, spectroscopy, and neuroscience. But that performance comes at a steep price. Ti: sapphire lasers are big, on the order of cubic feet in volume. They are expensive, costing hundreds of thousands of dollars each. And they require other high-powered lasers, themselves costing $30,000 each, to supply them with enough energy to function.

As a result, Ti: lasers have never achieved the broad, real-world adoption they deserve—until now. In a dramatic leap forward in scale, efficiency, and cost, researchers at Stanford University have built a Ti: sapphire laser on a chip. The prototype is four orders of magnitude smaller (10,000x) and three orders less expensive (1,000x) than any Ti: sapphire laser ever produced.

“This is a complete departure from the old model,” said Jelena Vučković, the Jensen Huang Professor in Global Leadership, a professor of electrical engineering, and senior author of the paper introducing the chip-scale Ti: sapphire laser published in the journal Nature.

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Researchers have developed a novel mechanical computer inspired by kirigami, utilizing interconnected polymer cubes for data storage without electronics.

This system allows for multiple stable states, enhancing binary computing to potentially include additional data states. The design, leveraging the principles of kirigami, enables complex data storage and computation structures with practical applications in mechanical encryption and haptic systems.

Mechanical Computing Innovation

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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.

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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.

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The artificial synapse array with an electrolyte-gated transistor (EGT) as an array unit presents considerable potential for neuromorphic computation. However, the integration of EGTs faces the drawback of the conflict between the polymer electrolytes and photo-lithography. This study presents a scheme based on a lateral-gate structure to realize high-density integration of EGTs and proposes the integration of 100 × 100 EGTs into a 2.5 × 2.5 cm2 glass, with a unit density of up to 1,600 devices cm-2. Furthermore, an electrolyte framework is developed to enhance the array performance, with ionic conductivity of up to 2.87 × 10-3 S cm-1 owing to the porosity of zeolitic imidazolate frameworks-67. The artificial synapse array realizes image processing functions, and exhibits high performance and homogeneity. The handwriting recognition accuracy of a representative device reaches 92.80%, with the standard deviation of all the devices being limited to 9.69%. The integrated array and its high performance demonstrate the feasibility of the scheme and provide a solid reference for the integration of EGTs.

Keywords: Photo-Lithography; artificial synapse array; electrolyte-gated transistors; lateral-gate; metal-organic framework.

© 2023 The Authors. Advanced Science published by Wiley-VCH GmbH.

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Current computing systems rely on Boolean logic and von Neumann architecture, where computing cells are based on high-speed electron-conducting complementary metal-oxide-semiconductor (CMOS) transistors. In contrast, ions play an essential role in biological neural computing. Compared with CMOS units, the synapse/neuron computing speed is much lower, but the human brain performs much better in many tasks such as pattern recognition and decision-making. Recently, ionic dynamics in oxide electrolyte-gated transistors have attracted increasing attention in the field of neuromorphic computing, which is more similar to the computing modality in the biological brain. In this review article, we start with the introduction of some ionic processes in biological brain computing. Then, electrolyte-gated ionic transistors, especially oxide ionic transistors, are briefly introduced. Later, we review the state-of-the-art progress in oxide electrolyte-gated transistors for ionic neuromorphic computing including dynamic synaptic plasticity emulation, spatiotemporal information processing, and artificial sensory neuron function implementation. Finally, we will address the current challenges and offer recommendations along with potential research directions.

Keywords: bio-inspired computing; ionic transistors; oxide semiconductors.

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Memristive devices with both electrically and optically induced synaptic dynamic behaviors will be crucial to the accomplishment of brain-inspired neuromorphic computing systems, in which the resistive materials and device architectures are two of the most important cornerstones, but still under challenge. Herein, kuramite Cu3SnS4 is newly introduced into poly-methacrylate as the switching medium to construct memristive devices, and the expected high-performance bio-mimicry of diverse optoelectronic synaptic plasticity is demonstrated. In addition to the excellent basic performances, such as stable bipolar resistive switching with On/Off ratio of ∼486, Set/Reset voltage of ∼-0.88/+0.96 V, and good retention feature of up to 104 s, the new designs of memristors possess not only the multi-level controllable resistive-switching memory property but also the capability of mimicking optoelectronic synaptic plasticity, including electrically and visible/near-infrared light-induced excitatory postsynaptic currents, short-/long-term memory, spike-timing-dependent plasticity, long-term plasticity/depression, short-term plasticity, paired-pulse facilitation, and “learning-forgetting-learning” behavior as well. Predictably, as a new class of switching medium material, such proposed kuramite-based artificial optoelectronic synaptic device has great potential to be applied to construct neuromorphic architectures in simulating human brain functions.

© 2023 Author(s). Published under an exclusive license by AIP Publishing.

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More than 3.5 billion years ago, life on Earth emerged from chemical reactions. Nature invented RNA, proteins, and DNA, the core molecules of life, and created the ribosome, a molecular factory that builds proteins from instructions in the genome.

Proteins are wondrous dynamic molecules with incredible functions—from molecular engines that power motion, to photosynthetic machines that capture light and convert it to energy, scaffolding that builds the internal skeletons of cells, complex sensors that interact with the environment, and information processing systems that run the programs and operating system of life. Proteins underlie disease and health, and many life-saving medicines are proteins.

Biology is the most advanced technology that has ever been created, far beyond anything that people have engineered. The ribosome is programmable—it takes the codes of proteins in the form of RNA and builds them up from scratch—fabrication at the atomic scale. Every cell in every organism on earth has thousands to millions of these molecular factories. But even the most sophisticated computational tools created to date barely scratch the surface: biology is written in a language we don’t yet understand.

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