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Статті в журналах з теми "Neuro inspiré"

Автор: Grafiati
Опубліковано: 8 червня 2024
Оновлено: 19 липня 2025

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1

Ghani, Arfan, Thomas Dowrick, and Liam J. McDaid. "OSPEN: an open source platform for emulating neuromorphic hardware." International Journal of Reconfigurable and Embedded Systems (IJRES) 12, no. 1 (2023): 1. http://dx.doi.org/10.11591/ijres.v12.i1.pp1-8.

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Анотація:
This paper demonstrates a framework that entails a bottom-up approach to accelerate research, development, and verification of neuro-inspired sensing devices for real-life applications. Previous work in neuromorphic engineering mostly considered application-specific designs which is a strong limitation for researchers to develop novel applications and emulate the true behaviour of neuro-inspired systems. Hence to enable the fully parallel brain-like computations, this paper proposes a methodology where a spiking neuron model was emulated in software and electronic circuits were then implemented and characterized. The proposed approach offers a unique perspective whereby experimental measurements taken from a fabricated device allowing empirical models to be developed. This technique acts as a bridge between the theoretical and practical aspects of neuro-inspired devices. It is shown through software simulations and empirical modelling that the proposed technique is capable of replicating neural dynamics and post-synaptic potentials. Retrospectively, the proposed framework offers a first step towards open-source neuro-inspired hardware for a range of applications such as healthcare, applied machine learning and the internet of things (IoT).
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2

Arfan, Ghani, Dowrick Thomas, and J. McDaid Liam. "OSPEN: an open source platform for emulating neuromorphic hardware." International Journal of Reconfigurable and Embedded Systems (IJRES) 12, no. 1 (2023): 1–8. https://doi.org/10.11591/ijres.v12.i1.pp1-8.

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Анотація:
This paper demonstrates a framework that entails a bottom-up approach to accelerate research, development, and verification of neuro-inspired sensing devices for real-life applications. Previous work in neuromorphic engineering mostly considered application-specific designs which is a strong limitation for researchers to develop novel applications and emulate the true behaviour of neuro-inspired systems. Hence to enable the fully parallel brain-like computations, this paper proposes a methodology where a spiking neuron model was emulated in software and electronic circuits were then implemented and characterized. The proposed approach offers a unique perspective whereby experimental measurements taken from a fabricated device allowing empirical models to be developed. This technique acts as a bridge between the theoretical and practical aspects of neuro-inspired devices. It is shown through software simulations and empirical modelling that the proposed technique is capable of replicating neural dynamics and post-synaptic potentials. Retrospectively, the proposed framework offers a first step towards open-source neuro-inspired hardware for a range of applications such as healthcare, applied machine learning and the internet of things (IoT).
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3

Zhang, Wenqiang, Bin Gao, Jianshi Tang, et al. "Neuro-inspired computing chips." Nature Electronics 3, no. 7 (2020): 371–82. http://dx.doi.org/10.1038/s41928-020-0435-7.

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4

Birzhanova, Aigerim, Aliya Nurgaliyeva, Azhar Nurmagambetova, Hasan Dinçer, and Serhat Yüksel. "Neuro quantum-inspired decision-making for investor perception in green and conventional bond investments." Investment Management and Financial Innovations 21, no. 1 (2024): 168–84. http://dx.doi.org/10.21511/imfi.21(1).2024.14.

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Анотація:
The purpose of this study is to make a comprehensive analysis of investor perceptions in the context of green and conventional bond investments. For this purpose, a new model is presented by considering two steps. First, a criteria set is generated by considering balanced scorecard perspectives that are finance, customer, organizational effectiveness and learning and growth. After that, the neuro Quantum fuzzy M-SWARA method is considered to weight these criteria. Secondly, seven critical determinants for bond investments are identified that are coupon rates, volume, maturity, riskiness, liquidity, volatility, and tax considerations. Neuro Quantum fuzzy TOPSIS approach is employed to rank these factors. The main contribution of the study is that by combining the balanced scorecard framework and quantum-inspired decision-making techniques, this paper offers a novel and sophisticated decision-making model to understanding investor behavior. Similarly, in the proposed model, a new methodology is generated by the name of M-SWARA. In this framework, some enhancements are adopted to the SWARA technique. The weighting results indicate that meeting customer expectations is the most critical factor that affects the investor perception to make investments to the bonds. Moreover, according to the ranking results, it is concluded that coupon rates are the most important item for both conventional and green bond investors. On the other hand, with respect to the conventional bond investor, tax is the second most essential factor. However, regarding the green bond investors, volatility plays a critical role. AcknowledgmentThis research has been/was/is funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (№ AP 19679105 “Transformation of ESG financial instruments in the context of the development of the green economy of the Republic of Kazakhstan”).
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5

Harkhoe, Krishan, Guy Verschaffelt, and Guy Van der Sande. "Neuro-Inspired Computing with Spin-VCSELs." Applied Sciences 11, no. 9 (2021): 4232. http://dx.doi.org/10.3390/app11094232.

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Анотація:
Delay-based reservoir computing (RC), a neuromorphic computing technique, has gathered lots of interest, as it promises compact and high-speed RC implementations. To further boost the computing speeds, we introduce and study an RC setup based on spin-VCSELs, thereby exploiting the high polarization modulation speed inherent to these lasers. Based on numerical simulations, we benchmarked this setup against state-of-the-art delay-based RC systems and its parameter space was analyzed for optimal performance. The high modulation speed enabled us to have more virtual nodes in a shorter time interval. However, we found that at these short time scales, the delay time and feedback rate heavily influence the nonlinear dynamics. Therefore, and contrary to other laser-based RC systems, the delay time has to be optimized in order to obtain good RC performances. We achieved state-of-the-art performances on a benchmark timeseries prediction task. This spin-VCSEL-based RC system shows a ten-fold improvement in processing speed, which can further be enhanced in a straightforward way by increasing the birefringence of the VCSEL chip.
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6

Zhong, Xiaopin, and Lin Ma. "A Neuro-inspired Adaptive Motion Detector." Optics and Photonics Journal 03, no. 02 (2013): 94–98. http://dx.doi.org/10.4236/opj.2013.32b024.

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7

Huang, Ping-Chen, and Jan M. Rabaey. "A Neuro-Inspired Spike Pattern Classifier." IEEE Journal on Emerging and Selected Topics in Circuits and Systems 8, no. 3 (2018): 555–65. http://dx.doi.org/10.1109/jetcas.2018.2842035.

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8

Kahol, Kanav, and Sethuraman Panchanathan. "Neuro-cognitively inspired haptic user interfaces." Multimedia Tools and Applications 37, no. 1 (2007): 15–38. http://dx.doi.org/10.1007/s11042-007-0167-y.

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9

GINGL, ZOLTAN, LASZLO B. KISH, and SUNIL P. KHATRI. "TOWARDS BRAIN-INSPIRED COMPUTING." Fluctuation and Noise Letters 09, no. 04 (2010): 403–12. http://dx.doi.org/10.1142/s0219477510000332.

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Анотація:
We present introductory considerations and analysis toward computing applications based on the recently introduced deterministic logic scheme with random spike (pulse) trains [Phys. Lett. A373 (2009) 2338–2342]. Also, in considering the questions, "why random?" and "why pulses?", we show that the random pulse based scheme provides the advantages of realizing multivalued deterministic logic. Pulse trains are realized by an element called orthogonator. We discuss two different types of orthogonators, parallel (intersection-based) and serial (demultiplexer-based) orthogonators. The last one can be slower but it makes sequential logic design straightforward. We propose generating a multidimensional logic hyperspace [Phys. Lett. A373 (2009) 1928–1934] by using the zero-crossing events of uncorrelated Gaussian electrical noises available in the chips. The spike trains in the hyperspace are non-overlapping, and are referred to as neuro-bits. To demonstrate this idea, we generate three-dimensional hyperspace bases using the zero-crossing events of two uncorrelated Gaussian noise sources. In such a scenario, the detection of different hyperspace basis elements may have vastly differing delays. We show that it is possible to provide an identical speed for the detection of all the hyperspace bases elements using correlated noise sources, and demonstrate this for the two neuro-bits situation. The key impact of this paper is to demonstrate that a logic design approach using such neuro-bits can yield a fast, low power and environmental variation tolerant means of designing computer circuitry. It also enables the realization of multivalued logic, and also significantly increasing the complexity of computer circuits by allowing several neuro-bits to be transmitted on a single wire.
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10

Hu, Jinming. "Bridging Neuroscience and AI: A Comprehensive Investigation of Brain-Inspired Computing Models." ITM Web of Conferences 73 (2025): 03001. https://doi.org/10.1051/itmconf/20257303001.

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Анотація:
Artificial Intelligence (AI) has reached new heights, supported by advancements in hardware and algorithm theory. Areas like robotics and autonomous driving have made significant strides, but brain-inspired computing remains a distinctive field. Although there were early hopes of AI closely connecting with brain science, this integration has been minimal. Neuroscience has mostly inspired some early algorithms, while most neural networks only adopted the idea of neuron connections without fully replicating real neural signals. However, brain-inspired algorithms, such as Spiking Neural Networks (SNNs), have shown promising results, often outperforming traditional algorithms in specific tasks and offering lower power consumption. These advancements could inspire new AI models or improve existing ones. This review explores the development of successful brain-inspired algorithms, starting with the structure and function of neurons, including cerebellar structures. It then discussed spiking neural networks, their principles, and recent research, as well as cerebellar-inspired models. Finally, the article summarizes methods for building these models and their applications in fields like robotics.
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11

Blachowicz, Tomasz, Jacek Grzybowski, Pawel Steblinski, and Andrea Ehrmann. "Neuro-Inspired Signal Processing in Ferromagnetic Nanofibers." Biomimetics 6, no. 2 (2021): 32. http://dx.doi.org/10.3390/biomimetics6020032.

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Анотація:
Computers nowadays have different components for data storage and data processing, making data transfer between these units a bottleneck for computing speed. Therefore, so-called cognitive (or neuromorphic) computing approaches try combining both these tasks, as is done in the human brain, to make computing faster and less energy-consuming. One possible method to prepare new hardware solutions for neuromorphic computing is given by nanofiber networks as they can be prepared by diverse methods, from lithography to electrospinning. Here, we show results of micromagnetic simulations of three coupled semicircle fibers in which domain walls are excited by rotating magnetic fields (inputs), leading to different output signals that can be used for stochastic data processing, mimicking biological synaptic activity and thus being suitable as artificial synapses in artificial neural networks.
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12

Yu, Shimeng. "Neuro-Inspired Computing With Emerging Nonvolatile Memorys." Proceedings of the IEEE 106, no. 2 (2018): 260–85. http://dx.doi.org/10.1109/jproc.2018.2790840.

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13

Dumitrache, Ioan, Simona Iuliana Caramihai, Mihnea Alexandru Moisescu, and Ioan Stefan Sacala. "Neuro-inspired Framework for cognitive manufacturing control." IFAC-PapersOnLine 52, no. 13 (2019): 910–15. http://dx.doi.org/10.1016/j.ifacol.2019.11.311.

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14

Rezk, Karen, and Catherine-Anne Miller. "Délais dans l’octroi des congés en neuro-oncologie : utilisation d’une approche inspirée des méthodes Lean Six Sigma pour en déterminer les causes internes." Canadian Oncology Nursing Journal 26, no. 3 (2016): 221–27. http://dx.doi.org/10.5737/23688076263221227.

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15

Henniquau, Dimitri, Pierre Falez, Philippe Devienne, et al. "Système de vision neuro-inspirée : Application à la vision artificielle." J3eA 21 (2022): 2035. http://dx.doi.org/10.1051/j3ea/20222035.

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Анотація:
L’architecture des systèmes numériques traditionnels est loin d’être optimale puisqu’un microprocesseur est tout autant une plaque chauffante qu’un calculateur (Intel Cooking [1]). Il devient donc urgent de proposer des architectures de traitement de l’information radicalement différentes, « neuro-inspirées », qui permettent d’apporter des fonctions cogni-tives aux solutions existantes. C’est ainsi que des neurones et synapses artificiels travaillant à faible tension d’alimentation ont été fabriqués, ce qui leur confère une très basse consommation d’énergie et une fabrication aisée. Ce stand montre à des jeunes lycéens et étudiants que l’utilisation de tels neurones et synapses dans un système de vision artificielle (capture et traitement d’images) conduira certainement à une forte amélioration des performances et, parallèlement, à une réduc-tion drastique de la consommation énergétique. Il s’agit d’une expérience pédagogique innovante, riche de nombreux supports variés, afin de transmettre aux jeunes générations les enjeux des activités de recherche qui se construisent dans les laboratoires de l’Université de Lille.
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16

Marco-Detchart, Cedric, Giancarlo Lucca, Carlos Lopez-Molina, Laura De Miguel, Graçaliz Pereira Dimuro, and Humberto Bustince. "Neuro-inspired edge feature fusion using Choquet integrals." Information Sciences 581 (December 2021): 740–54. http://dx.doi.org/10.1016/j.ins.2021.10.016.

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17

Wang, Panni, and Shimeng Yu. "Ferroelectric devices and circuits for neuro-inspired computing." MRS Communications 10, no. 4 (2020): 538–48. http://dx.doi.org/10.1557/mrc.2020.71.

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18

Shi, Yuanhong, Qilin Hua, Zilong Dong, et al. "Neuro-inspired thermoresponsive nociceptor for intelligent sensory systems." Nano Energy 113 (August 2023): 108549. http://dx.doi.org/10.1016/j.nanoen.2023.108549.

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19

He, Yongli, Yixin Zhu, and Qing Wan. "Oxide Ionic Neuro-Transistors for Bio-inspired Computing." Nanomaterials 14, no. 7 (2024): 584. http://dx.doi.org/10.3390/nano14070584.

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Анотація:
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.
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20

Liu, Shuang, Guangyao Wang, Tianshuo Bai, et al. "Magnetic Skyrmion-Based Spiking Neural Network for Pattern Recognition." Applied Sciences 12, no. 19 (2022): 9698. http://dx.doi.org/10.3390/app12199698.

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Анотація:
Spiking neural network (SNN) has emerged as one of the most powerful brain-inspired computing paradigms in complex pattern recognition tasks that can be enabled by neuromorphic hardware. However, owing to the fundamental architecture mismatch between biological and Boolean logic, CMOS implementation of SNN is energy inefficient. A low-power approach with novel “neuro-mimetic” devices offering a direct mapping to synaptic and neuronal functionalities is still an open area. In this paper, SNN constructed with novel magnetic skyrmion-based leaky-integrate-fire (LIF) spiking neuron and the skyrmionic synapse crossbar is proposed. We perform a systematic device-circuit-architecture co-design for pattern recognition to evaluate the feasibility of our proposal. The simulation results demonstrated that our device has superior lower switching voltage and high energy efficiency, two times lower programming energy efficiency in comparison with CMOS devices. This work paves a novel pathway for low-power hardware design using full-skyrmion SNN architecture, as well as promising avenues for implementing neuromorphic computing schemes.
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21

Wang, Qiang, Gang Niu, Wei Ren, et al. "Phase Change Random Access Memory for Neuro‐Inspired Computing." Advanced Electronic Materials 7, no. 6 (2021): 2001241. http://dx.doi.org/10.1002/aelm.202001241.

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22

Kuzum, Duygu. "Neuro-Inspired Computing with Resistive Switching Devices [Guest Editorial]." IEEE Nanotechnology Magazine 12, no. 3 (2018): 4. http://dx.doi.org/10.1109/mnano.2018.2849799.

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23

Chabi, Djaafar, Damien Querlioz, Weisheng Zhao, and Jacques-Olivier Klein. "Robust learning approach for neuro-inspired nanoscale crossbar architecture." ACM Journal on Emerging Technologies in Computing Systems 10, no. 1 (2014): 1–20. http://dx.doi.org/10.1145/2539123.

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24

Shoureshi, Rahmat A., Tracy Schantz, and Sun W. Lim. "Bio-inspired neuro-symbolic approach to diagnostics of structures." Smart Structures and Systems 7, no. 3 (2011): 229–40. http://dx.doi.org/10.12989/sss.2011.7.3.229.

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25

Moghaddam, Mohsen, Qiliang Chen, and Abhijit V. Deshmukh. "A neuro-inspired computational model for adaptive fault diagnosis." Expert Systems with Applications 140 (February 2020): 112879. http://dx.doi.org/10.1016/j.eswa.2019.112879.

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26

HAMMAD, ABDALLAH, SIMON X. YANG, M. TAREK ELEWA, HALA MANSOUR, and SALAH ALI. "VIRTUAL INSTRUMENTATION BASED SYSTEMS FOR REAL-TIME PATH PLANNING OF MOBILE ROBOTS USING BIO-INSPIRED NEURAL NETWORKS." International Journal of Computational Intelligence and Applications 10, no. 03 (2011): 357–75. http://dx.doi.org/10.1142/s1469026811003148.

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Анотація:
In this paper, novel virtual instrumentation based systems for real-time collision-free path planning and tracking control of mobile robots are proposed. The developed virtual instruments are computationally simple and efficient in comparison to other approaches, which act as a new soft-computing platform to implement a biologically-inspired neural network. This neural network is topologically arranged with only local lateral connections among neurons. The dynamics of each neuron is described by a shunting equation with both excitatory and inhibitory connections. The neural network requires no off-line training or on-line learning, which is capable of planning a comfortable trajectory to the target without suffering from neither the too close nor the too far problems. LabVIEW is chosen as the software platform to build the proposed virtual instrumentation systems, as it is one of the most important industrial platforms. We take the initiative to develop the first neuro-dynamic application in LabVIEW. The developed virtual instruments could be easily used as educational and research tools for studying various robot path planning and tracking situations that could be easily understood and analyzed step by step. The effectiveness and efficiency of the developed virtual instruments are demonstrated through simulation and comparison studies.
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27

Farquhar, E., and P. Hasler. "A bio-physically inspired silicon neuron." IEEE Transactions on Circuits and Systems I: Regular Papers 52, no. 3 (2005): 477–88. http://dx.doi.org/10.1109/tcsi.2004.842871.

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28

Mozaffari, Ahmad, Alireza Fathi, and Saeed Behzadipour. "An evolvable self-organizing neuro-fuzzy multilayered classifier with group method data handling and grammar-based bio-inspired supervisors for fault diagnosis of hydraulic systems." International Journal of Intelligent Computing and Cybernetics 7, no. 1 (2014): 38–78. http://dx.doi.org/10.1108/ijicc-06-2013-0034.

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Purpose – The purpose of this paper is to apply a hybrid neuro-fuzzy paradigm called self-organizing neuro-fuzzy multilayered classifier (SONeFMUC) to classify the operating faults of a hydraulic system. The main motivation behind the use of SONeFMUC is to attest the capabilities of neuro-fuzzy classifier for handling the difficulties associated with fault diagnosis of hydraulic circuits. Design/methodology/approach – In the proposed methodology, first, the neuro-fuzzy nodes at each layer of the SONeFMUC are trained separately using two well-known bio-inspired algorithms, i.e. a semi deterministic method with random walks called co-variance matrix adaptation evolutionary strategy (CMA-ES) and a swarm-based explorer with adaptive fuzzified parameters (SBEAFP). Thereafter, a revised version of the group method data handling (GMDH) policy that uses the Darwinian concepts such as truncation selection and elitism is engaged to connect the nodes of different layers in an effective manner. Findings – Based on comparative numerical experiments, the authors conclude that integration of neuro-fuzzy method and bio-inspired supervisor results in a really powerful classification tool beneficial for uncertain environments. It is proved that the method outperforms some well-known classifiers such as support vector machine (SVM) and particle swarm optimization-based SVM (PSO-SVM). Besides, it is indicated that an efficient bio-inspired method can effectively adjust the constructive parameters of the multi-layered neuro-fuzzy classifier. For the case, it is observed that designing a fuzzy controller for PSO predisposes it to effectively balance the exploration/exploitation capabilities, and consequently optimize the structure of SONeFMUC. Originality/value – The originality of the paper can be considered from both numerical and practical points of view. The signals obtained through the data acquisition possess six different features in order for the hydraulic system to undergo four types of faults, i.e. cylinder fault, pump fault, valve leakage fault and rupture of the piping system. Besides, to elaborate on the authenticity and efficacy of the proposed method, its performance is compared with well-known rival techniques.
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29

Dominguez-Morales, Manuel, Juan P. Domínguez-Morales, Ángel Jiménez-Fernández, Alejandro Linares-Barranco, and Gabriel Jiménez-Moreno. "Stereo Matching in Address-Event-Representation (AER) Bio-Inspired Binocular Systems in a Field-Programmable Gate Array (FPGA)." Electronics 8, no. 4 (2019): 410. http://dx.doi.org/10.3390/electronics8040410.

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Анотація:
In stereo-vision processing, the image-matching step is essential for results, although it involves a very high computational cost. Moreover, the more information is processed, the more time is spent by the matching algorithm, and the more inefficient it is. Spike-based processing is a relatively new approach that implements processing methods by manipulating spikes one by one at the time they are transmitted, like a human brain. The mammal nervous system can solve much more complex problems, such as visual recognition by manipulating neuron spikes. The spike-based philosophy for visual information processing based on the neuro-inspired address-event-representation (AER) is currently achieving very high performance. The aim of this work was to study the viability of a matching mechanism in stereo-vision systems, using AER codification and its implementation in a field-programmable gate array (FPGA). Some studies have been done before in an AER system with monitored data using a computer; however, this kind of mechanism has not been implemented directly on hardware. To this end, an epipolar geometry basis applied to AER systems was studied and implemented, with other restrictions, in order to achieve good results in a real-time scenario. The results and conclusions are shown, and the viability of its implementation is proven.
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30

Adetomi, Adewale, Mohsin Raza, Khubaib Ahmed, Tughrul Arslan, Amir Hussain, and Ahsan Adeel. "Towards two-point neuron-driven energy-efficient multimodal open master hearing aid." Journal of the Acoustical Society of America 154, no. 4_supplement (2023): A32. http://dx.doi.org/10.1121/10.0022698.

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Here we demonstrate a two-point neuron-inspired audio-visual (AV) open Master Hearing Aid (openMHA) framework for on-chip energy-efficientspeech enhancement (SE). The developed system is compared against state-of-the-art cepstrum-based audio-only (A-only) SE and conventional point-neuron inspired deep neural net (DNN) driven multimodal (MM) SE. Pilot experiments demonstrate that the proposed system outperforms audio-only SE in terms of speech quality and intelligibility and performs comparably to point neuron-inspired DNN with a significantly reduced energy consumption at any time, both during training and inferencing.
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31

Hafsi, Bilel, Rabii Elmissaoui, and Adel Kalboussi. "Neural Network Based on SET Inverter Structures: Neuro-Inspired Memory." World Journal of Nano Science and Engineering 04, no. 04 (2014): 134–42. http://dx.doi.org/10.4236/wjnse.2014.44017.

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32

Mahmoudi, Maryam Tayefeh, Fattaneh Taghiyareh, and Babak N. Araabi. "A neuro-fuzzy immune inspired classifier for task-oriented texts." Journal of Intelligent & Fuzzy Systems 25, no. 3 (2013): 673–83. http://dx.doi.org/10.3233/ifs-120674.

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33

Hamilton, Tara Julia, Saeed Afshar, Andre van Schaik, and Jonathan Tapson. "Stochastic Electronics: A Neuro-Inspired Design Paradigm for Integrated Circuits." Proceedings of the IEEE 102, no. 5 (2014): 843–59. http://dx.doi.org/10.1109/jproc.2014.2310713.

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34

Corchado, E., and M. Wozniak. "Editorial: Neuro-symbolic Algorithms and Models for Bio-inspired Systems." Logic Journal of IGPL 19, no. 2 (2010): 289–92. http://dx.doi.org/10.1093/jigpal/jzq026.

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35

Galluccio, Laura, Sergio Palazzo, and G. Enrico Santagati. "Characterization of molecular communications among implantable biomedical neuro-inspired nanodevices." Nano Communication Networks 4, no. 2 (2013): 53–64. http://dx.doi.org/10.1016/j.nancom.2013.03.001.

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36

Tang, Huajin, Rui Yan, and Kay Chen Tan. "Cognitive Navigation by Neuro-Inspired Localization, Mapping, and Episodic Memory." IEEE Transactions on Cognitive and Developmental Systems 10, no. 3 (2018): 751–61. http://dx.doi.org/10.1109/tcds.2017.2776965.

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37

Guglielmelli, E. "S6.2 Neurorobotics: understanding the brain by building neuro-inspired robots." Clinical Neurophysiology 122 (June 2011): S14. http://dx.doi.org/10.1016/s1388-2457(11)60045-x.

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38

Zhang, Wenbin, Peng Yao, Bin Gao, et al. "Edge learning using a fully integrated neuro-inspired memristor chip." Science 381, no. 6663 (2023): 1205–11. http://dx.doi.org/10.1126/science.ade3483.

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Анотація:
Learning is highly important for edge intelligence devices to adapt to different application scenes and owners. Current technologies for training neural networks require moving massive amounts of data between computing and memory units, which hinders the implementation of learning on edge devices. We developed a fully integrated memristor chip with the improvement learning ability and low energy cost. The schemes in the STELLAR architecture, including its learning algorithm, hardware realization, and parallel conductance tuning scheme, are general approaches that facilitate on-chip learning by using a memristor crossbar array, regardless of the type of memristor device. Tasks executed in this study included motion control, image classification, and speech recognition.
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39

Soures, Nicholas, Vedant Karia, and Dhireesha Kudithipudi. "Advancing Neuro-Inspired Lifelong Learning for Edge with Co-Design." Proceedings of the AAAI Symposium Series 3, no. 1 (2024): 317. http://dx.doi.org/10.1609/aaaiss.v3i1.31226.

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Lifelong learning, which refers to an agent's ability to continuously learn and enhance its performance over its lifespan, is a significant challenge in artificial intelligence (AI), that biological systems tackle efficiently. This challenge is further exacerbated when AI is deployed in untethered environments with strict energy and latency constraints. We take inspiration from neural plasticity and investigate how to leverage and build energy-efficient lifelong learning machines. Specifically, we study how a combination of neural plasticity mechanisms, namely neuromodulation, synaptic consolidation, and metaplasticity, enhance the continual learning capabilities of AI models. We further co-design architectures that leverage compute-in-memory topologies and sparse spike-based communication with quantization for the edge. Aspects of this co-design can be transferred to federated lifelong learning scenarios.
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40

Al Harrach, Mariam, Maxime Yochum, and Fabrice Wendling. "NeoCoMM: Neocortical neuro-inspired computational model for realistic microscale simulations." SoftwareX 30 (May 2025): 102108. https://doi.org/10.1016/j.softx.2025.102108.

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41

Krestinskaya, O., and A. P. James. "Analogue neuro-memristive convolutional dropout nets." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 476, no. 2242 (2020): 20200210. http://dx.doi.org/10.1098/rspa.2020.0210.

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Randomly switching neurons ON/OFF while training and inference process is an interesting characteristic of biological neural networks, that potentially results in inherent adaptability and creativity expressed by human mind. Dropouts inspire from this random switching behaviour and in the artificial neural network they are used as a regularization techniques to reduce the impact of over-fitting during the training. The energy-efficient digital implementations of convolutional neural networks (CNN) have been on the rise for edge computing IoT applications. Pruning larger networks and optimization for performance accuracy has been the main direction of work in this field. As opposed to this approach, we propose to build a near-sensor analogue CNN with high-density memristor crossbar arrays. Since several active elements such as amplifiers are used in analogue designs, energy efficiency becomes a main challenge. To address this, we extend the idea of using dropouts in training to also the inference stage. The CNN implementations require a subsampling layer, which is implemented as a mean pooling layer in the design to ensure lower energy consumption. Along with the dropouts, we also investigate the effect of non-idealities of memristor and that of the network.
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42

Ding, Keyuan, Jiangjing Wang, Yuxing Zhou, et al. "Phase-change heterostructure enables ultralow noise and drift for memory operation." Science 366, no. 6462 (2019): 210–15. http://dx.doi.org/10.1126/science.aay0291.

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Artificial intelligence and other data-intensive applications have escalated the demand for data storage and processing. New computing devices, such as phase-change random access memory (PCRAM)–based neuro-inspired devices, are promising options for breaking the von Neumann barrier by unifying storage with computing in memory cells. However, current PCRAM devices have considerable noise and drift in electrical resistance that erodes the precision and consistency of these devices. We designed a phase-change heterostructure (PCH) that consists of alternately stacked phase-change and confinement nanolayers to suppress the noise and drift, allowing reliable iterative RESET and cumulative SET operations for high-performance neuro-inspired computing. Our PCH architecture is amenable to industrial production as an intrinsic materials solution, without complex manufacturing procedure or much increased fabrication cost.
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43

Feldhoff, Frank, and Hannes Toepfer. "Niobium Neuron: RSFQ Based Bio-Inspired Circuit." IEEE Transactions on Applied Superconductivity 31, no. 5 (2021): 1–5. http://dx.doi.org/10.1109/tasc.2021.3063212.

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44

Wang, Yuwei, Yi Zeng, Jianbo Tang, and Bo Xu. "Biological Neuron Coding Inspired Binary Word Embeddings." Cognitive Computation 11, no. 5 (2019): 676–84. http://dx.doi.org/10.1007/s12559-019-09643-1.

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45

Pruthi, Dimple, and Rashmi Bhardwaj. "Modeling air quality index using optimized neuronal networks inspired by swarms." Environmental Engineering Research 26, no. 6 (2020): 200469–0. http://dx.doi.org/10.4491/eer.2020.469.

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Air quality prediction is a significant field in environmental engineering, as air and water are essential for life on Earth. Nowadays, a common parameter used worldwide to measure air quality is termed as Air quality index. The parameter is measured based on the air pollutant concentration. The hybrid neuronal networks have been widely used for modeling air quality index. In the quest of optimizing the error in modeling air quality index, the existing adaptive neuro-fuzzy inference system is improved in this study using algorithms based on evolution and swarm movement. The model is based on the prominent air pollutants- nitrogen oxide, particulate matter of size equal to or less than 2.5microns (PM2.5), and sulphur dioxide. The proposed hybrid model using wavelet transform, particle swarm optimization, and adaptive neuro-fuzzy inference system accurately predicts the Air Quality Index and can be used in the public interest to take necessary precautions beforehand.
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46

Djahafi, Fatiha, and Abdelkader Gafour. "Neuro-Immune Model Based on Bio-Inspired Methods for Medical Diagnosis." International Journal of Ambient Computing and Intelligence 13, no. 1 (2022): 1–18. http://dx.doi.org/10.4018/ijaci.293176.

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In this article, a hybrid bio-inspired algorithm called neuro-immune is proposed based on Multi-Layer Perceptron Neural Network (MLPNN) and the Clonal Selection Classification (CSC) principle of the Artificial Immune System (AIS) for the classifying and diagnosing of medical disease. The proposed approach consists in the first phase to code the weights and biases of MLPNN concatenation vector of the input samples into an antigen vector and to decompose it into new weights to generate population memory cells which will be applied by the processes of the CSC algorithm clone and mutate in the second phase, to optimize the accuracy class of data and updating the MLPNN weights to minimize the mean squared error. Experimental results show that the proposed hybrid neuro-immune model allows obtaining a high diagnosis performance on a set of medical data problems from the UCI repository with an improved classification accuracy compared to existing works in the literature.
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47

Yan, Yan, Kamen Ivanov, Olatunji Mumini Omisore, et al. "Gait Rhythm Dynamics for Neuro-Degenerative Disease Classification via Persistence Landscape- Based Topological Representation." Sensors 20, no. 7 (2020): 2006. http://dx.doi.org/10.3390/s20072006.

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Neuro-degenerative disease is a common progressive nervous system disorder that leads to serious clinical consequences. Gait rhythm dynamics analysis is essential for evaluating clinical states and improving quality of life for neuro-degenerative patients. The magnitude of stride-to-stride fluctuations and corresponding changes over time—gait dynamics—reflects the physiology of gait, in quantifying the pathologic alterations in the locomotor control system of health subjects and patients with neuro-degenerative diseases. Motivated by algebra topology theory, a topological data analysis-inspired nonlinear framework was adopted in the study of the gait dynamics. Meanwhile, the topological representation–persistence landscapes were used as input of classifiers in order to distinguish different neuro-degenerative disease type from healthy. In this work, stride-to-stride time series from healthy control (HC) subjects are compared with the gait dynamics from patients with amyotrophic lateral sclerosis (ALS), Huntington’s disease (HD), and Parkinson’s disease (PD). The obtained results show that the proposed methodology discriminates healthy subjects from subjects with other neuro-degenerative diseases with relatively high accuracy. In summary, our study is the first attempt to provide a topological representation-based method into the disease classification with gait rhythms measured from the stride intervals to visualize gait dynamics and classify neuro-degenerative diseases. The proposed method could be potentially used in earlier interventions and state monitoring.
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48

Luo, Yuan-Chun, Jae Hur, and Shimeng Yu. "Ferroelectric Tunnel Junction Based Crossbar Array Design for Neuro-Inspired Computing." IEEE Transactions on Nanotechnology 20 (2021): 243–47. http://dx.doi.org/10.1109/tnano.2021.3066319.

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49

Wang, Cheng, Chankyu Lee, and Kaushik Roy. "Noise resilient leaky integrate-and-fire neurons based on multi-domain spintronic devices." Scientific Reports 12, no. 1 (2022). http://dx.doi.org/10.1038/s41598-022-12555-0.

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AbstractThe capability of emulating neural functionalities efficiently in hardware is crucial for building neuromorphic computing systems. While various types of neuro-mimetic devices have been investigated, it remains challenging to provide a compact device that can emulate spiking neurons. In this work, we propose a non-volatile spin-based device for efficiently emulating a leaky integrate-and-fire neuron. By incorporating an exchange-coupled composite free layer in spin-orbit torque magnetic tunnel junctions, multi-domain magnetization switching dynamics is exploited to realize gradual accumulation of membrane potential for a leaky integrate-and-fire neuron with compact footprints. The proposed device offers significantly improved scalability compared with previously proposed spin-based neuro-mimetic implementations while exhibiting high energy efficiency and good controllability. Moreover, the proposed neuron device exhibits a varying leak constant and a varying membrane resistance that are both dependent on the magnitude of the membrane potential. Interestingly, we demonstrate that such device-inspired dynamic behaviors can be incorporated to construct more robust spiking neural network models, and find improved resiliency against various types of noise injection scenarios. The proposed spintronic neuro-mimetic devices may potentially open up exciting opportunities for the development of efficient and robust neuro-inspired computational hardware.
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50

Huang, Jinqi, Spyros Stathopoulos, Alexantrou Serb, and Themis Prodromakis. "NeuroPack: An Algorithm-Level Python-Based Simulator for Memristor-Empowered Neuro-Inspired Computing." Frontiers in Nanotechnology 4 (April 20, 2022). http://dx.doi.org/10.3389/fnano.2022.851856.

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Анотація:
Emerging two-terminal nanoscale memory devices, known as memristors, have demonstrated great potential for implementing energy-efficient neuro-inspired computing architectures over the past decade. As a result, a wide range of technologies have been developed that, in turn, are described via distinct empirical models. This diversity of technologies requires the establishment of versatile tools that can enable designers to translate memristors’ attributes in novel neuro-inspired topologies. In this study, we present NeuroPack, a modular, algorithm-level Python-based simulation platform that can support studies of memristor neuro-inspired architectures for performing online learning or offline classification. The NeuroPack environment is designed with versatility being central, allowing the user to choose from a variety of neuron models, learning rules, and memristor models. Its hierarchical structure empowers NeuroPack to predict any memristor state changes and the corresponding neural network behavior across a variety of design decisions and user parameter options. The use of NeuroPack is demonstrated herein via an application example of performing handwritten digit classification with the MNIST dataset and an existing empirical model for metal-oxide memristors.
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