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Heat dissipation is a natural consequence of operating any electronic system. In nearly all computing systems, such heat is usually minimized by design and cooling. Here, we show that the temporal dynamics of internally produced heat in electronic devices can be engineered to both encode information within a single device and process information across multiple devices. In our demonstration, electronic NbO Mott neurons, integrated on a flexible organic substrate, exhibit 18 biomimetic neuronal behaviours and frequency-based nociception within a single component by exploiting both the thermal dynamics of the Mott transition and the dynamical thermal interactions with the organic substrate. Further, multiple interconnected Mott neurons spatiotemporally communicate purely via heat, which we use for graph optimization by consuming over 10 times less energy when compared with the best digital processors. Thus, exploiting natural thermal processes in computing can lead to functionally dense, energy-efficient and radically novel mixed-physics computing primitives.
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http://dx.doi.org/10.1038/s41563-024-01913-0 | DOI Listing |
Small
August 2025
State Key Laboratory of Wide Band Gap Semiconductor Devices and Integrated Technology, School of Microelectronics, Xidian University, Xi'an, 710071, China.
Multi-band image fusion in biological systems aims to integrate image data from various spectral bands to obtain more comprehensive, accurate, and effective image information. However, developing efficient and low-power artificial vision multi-band image fusion systems inspired by biological vision systems remains a challenge. Here, an artificial visual neuron based on the integration of InO/PY-IT phototransistor and NbO Mott memristor is proposed, which can simultaneously sense optical signals in the UV and near-infrared bands and achieve pulse encoding of different frequencies through light stimulation of different intensities.
View Article and Find Full Text PDFAdv Mater
August 2025
Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.
The human brain's efficiency and adaptability in processing information is largely attributed to spatiotemporal spiking activities and intrinsic plasticity-the ability of neurons to autonomously modulate their excitability. Mott memristors, with their threshold switching characteristics, have been effectively utilized as artificial neurons, or neuristors, to generate spiking activities. However, the implementation of intrinsic plasticity and its significance in neuromorphic computing has yet to be systematically explored.
View Article and Find Full Text PDFSocial hierarchy is an evolutionarily-conserved phenomenon determined by social dominance behavior that has profound influence on health and relevance in neuropsychiatric disorders. Despite this, the neural mechanisms underlying social dominance remain unclear and current behavioral tests are limited. Here, we describe a novel platform test of social dominance where mice compete for space on a small, elevated platform surrounded by cold water and rank is calculated by total time spent on platform.
View Article and Find Full Text PDFFront Neurosci
July 2025
Department of Quantum and Computing Engineering, Delft University of Technology, Delft, Netherlands.
Introduction: In 2012, potassium and sodium ion channels in Hodgkin-Huxley-based brain models were shown to exhibit memristive behavior. This positioned memristors as strong candidates for implementing biologically accurate artificial neurons. Memristor-based brain simulations offer advantages in energy efficiency, scalability, and compactness, benefiting fields such as soft robotics, embedded systems, and neuroprosthetics.
View Article and Find Full Text PDFNanomaterials (Basel)
July 2025
School of Physics and Electronic Engineering, Shanxi Key Laboratory of Wireless Communication and Detection, Shanxi University, Taiyuan 030006, China.
Threshold-switching memristors (TSMs) are emerging as key enablers for hardware spiking neural networks, offering intrinsic spiking dynamics, sub-pJ energy consumption, and nanoscale footprints ideal for brain-inspired computing at the edge. This review provides a comprehensive examination of how TSMs emulate diverse spiking behaviors-including oscillatory, leaky integrate-and-fire (LIF), Hodgkin-Huxley (H-H), and stochastic dynamics-and how these features enable compact, energy-efficient neuromorphic systems. We analyze the physical switching mechanisms of redox and Mott-type TSMs, discuss their voltage-dependent dynamics, and assess their suitability for spike generation.
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