Synaptic Resistor Circuits Based on Al Oxide and Ti Silicide for Concurrent Learning and Signal Processing in Artificial Intelligence Systems

Gao, Dawei; Shenoy, Rahul; Yi, Su-in; Lee, Jungmin; Xu, Mingjie; Rong, Zixuan; Deo, Atharva; Nathan, Dhruva; Zheng, Jian‐Guo; Williams, R. Stanley; Chen, Yong

doi:10.1002/adma.202210484

Cited by 7 publications

(3 citation statements)

References 46 publications

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“…In the past 200 years, various techniques for studying epilepsy neural circuits have emerged, and it is inevitable that the authors missed some of them in their searching process, such as contributions from surgery and psychology (Guo et al 2023 ; Gunn and Baram 2017 ; Mao et al 2022 ). Additionally, the latest research, such as investigating neuronal connection patterns through AI algorithms and using them as the basis for functional operation, was not discussed separately due to limited literature (Gao et al 2023 ; Cao et al 2023 ; Zhang et al 2021b ). Undoubtedly, these technologies are also very significant for the study of epilepsy.…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

Unraveling the Neural Circuits: Techniques, Opportunities and Challenges in Epilepsy Research

Xiao,

Li,

Kong

et al. 2024

Cell Mol Neurobiol

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Epilepsy, a prevalent neurological disorder characterized by high morbidity, frequent recurrence, and potential drug resistance, profoundly affects millions of people globally. Understanding the microscopic mechanisms underlying seizures is crucial for effective epilepsy treatment, and a thorough understanding of the intricate neural circuits underlying epilepsy is vital for the development of targeted therapies and the enhancement of clinical outcomes. This review begins with an exploration of the historical evolution of techniques used in studying neural circuits related to epilepsy. It then provides an extensive overview of diverse techniques employed in this domain, discussing their fundamental principles, strengths, limitations, as well as their application. Additionally, the synthesis of multiple techniques to unveil the complexity of neural circuits is summarized. Finally, this review also presents targeted drug therapies associated with epileptic neural circuits. By providing a critical assessment of methodologies used in the study of epileptic neural circuits, this review seeks to enhance the understanding of these techniques, stimulate innovative approaches for unraveling epilepsy's complexities, and ultimately facilitate improved treatment and clinical translation for epilepsy. Graphical Abstract

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Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

Unraveling the Neural Circuits: Techniques, Opportunities and Challenges in Epilepsy Research

Xiao,

Li,

Kong

et al. 2024

Cell Mol Neurobiol

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“…The training process of ANNs involves adjusting the weights and biases to enable the network to learn the complex mapping between inputs and outputs. ANNs find wide-ranging applications in materials science tasks like material exploration and discovery, prediction of material properties, optimization of material preparation and processing, correlation between material structure and properties, among others [21,22,[29][30][31]. Their advantages include the ability to automatically learn feature representations, nonlinear-modeling capabilities, and a certain degree of robustness to noise.…”

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

Artificial Neural Network-Based Prediction and Morphological Evolution of Cu2O Crystal Surface Energy

Shi,

Wang,

Zhou

et al. 2023

Coatings

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In this study, we investigate the crystal structure, surface energy, and atomic arrangement of Cu2O. Understanding these properties is crucial for exploring the potential applications and understanding the behavior of this material. We employ the Wulff construction method and an artificial neural network (ANN) model to analyze the relative surface energies of different crystal facets and predict the surface energy of Cu2O. The ANN model exhibits excellent performance, demonstrating its effectiveness in predicting material properties and providing automated feature-learning and nonlinear-modeling capabilities. Moreover, we analyze the atomic arrangements on various crystal facets and observe the presence of oxygen atoms on the {100} facet, as well as exposed under-coordinated copper atoms on the {111} and {110} facets. High-index facets such as {211} exhibit a higher atomic step density and screw dislocation density. By precisely controlling the synthesis process, it is possible to manipulate the proportion of high-index facets. These findings highlight the significance of understanding the surface energy and atomic arrangement of Cu2O crystals for comprehending their properties and surface reactions. In summary, this study provides valuable insights into the crystal structure, surface energy, and atomic arrangement of Cu2O, offering inspiration for its properties and potential applications. The combination of the Wulff construction method and ANN modeling provides a comprehensive understanding of Cu2O crystals and their surface behavior, contributing to the field of materials science and laying the foundation for various future applications utilizing the unique properties of Cu2O.

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“…[4,5] In this case, DOI: 10.1002/adfm.202309910 artificial synapse (i.e., neuromorphic device) is developed to realize some braininspired function, such as computing-inmemory and ultra-low power consumption, via duplicating the biological behaviors of synapse, neuron and biological instinct. [6] Recently, more and more synapse/neuromorphic devices are reported with the structure of two-terminal memristors and three terminal transistors based on emerging materials, including inorganic materials (e.g., ZnO, [7] WO 3−x [8] and TiO 𝛽 /Al 2 O 𝛼 /SiO 2 [9] ), organic material (e.g., COF-Azu [10] C10-DNTT, [11] and BTBTT6-syn [12] ) and low-dimension materials (e.g., mixed quantum dot, [13] p-n cross nanowire, [14] and 2D 𝛼-In 2 Se 3 [15,16] ), in order to mimic the function of synapse and neuron. For these electronic devices, multifunctional material can give them more intelligent behaviors, in which various sensing of the materials are developed to simulate the human sensory system, such as visual, auditory, olfactory, gustatory, tactile and pain.…”

Section: Introductionmentioning

confidence: 99%

Photoferroelectric Perovskite Synapses for Neuromorphic Computing

Han,

Ma,

et al. 2023

Adv Funct Materials

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Halide perovskite is an emerging material with excellent optoelectronic properties, and also widely used in neuromorphic devices. Recently, halide perovskite has been redefined as exhibiting extraordinary multifunction, e.g., photoferroelectricity. Herein, this work employs a composite material consisting of halide perovskite and organic ferroelectric material to develop a new photoferroelectric synapse, and the photoferroelectricity and some synaptic plasticity are investigated. By the corresponding test analysis, it is demonstrated that photoelectricity and ferroelectricity can reinforce each other in this photoferroelectric composite material. Versatile synaptic behaviors of the nervous system, including paired‐pulse facilitation/paired‐pulse depression, post‐tetanic potentiation /post‐tetanic depression, and spiking‐rate‐dependent plasticity, are successfully simulated. Particularly, the classical conditioning in Pavlov's dog experiment can be replicated in the photoferroelectric synapse to realize the learning function of the brain, including memory loss and recovery. This work could be conducive to the application of multifunctional perovskite materials in synapse devices and neuromorphic computing.

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Synaptic Resistor Circuits Based on Al Oxide and Ti Silicide for Concurrent Learning and Signal Processing in Artificial Intelligence Systems

Cited by 7 publications

References 46 publications

Unraveling the Neural Circuits: Techniques, Opportunities and Challenges in Epilepsy Research

Unraveling the Neural Circuits: Techniques, Opportunities and Challenges in Epilepsy Research

Artificial Neural Network-Based Prediction and Morphological Evolution of Cu2O Crystal Surface Energy

Photoferroelectric Perovskite Synapses for Neuromorphic Computing

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