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Articles de revues sur le sujet « Neuro inspiré »

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Auteur : Grafiati

Publié le 8 juin 2024

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1

Ghani, Arfan, Thomas Dowrick et Liam J. McDaid. « OSPEN : an open source platform for emulating neuromorphic hardware ». International Journal of Reconfigurable and Embedded Systems (IJRES) 12, n^o 1 (1 mars 2023) : 1. http://dx.doi.org/10.11591/ijres.v12.i1.pp1-8.

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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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Zhang, Wenqiang, Bin Gao, Jianshi Tang, Peng Yao, Shimeng Yu, Meng-Fan Chang, Hoi-Jun Yoo, He Qian et Huaqiang Wu. « Neuro-inspired computing chips ». Nature Electronics 3, n^o 7 (juillet 2020) : 371–82. http://dx.doi.org/10.1038/s41928-020-0435-7.

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Birzhanova, Aigerim, Aliya Nurgaliyeva, Azhar Nurmagambetova, Hasan Dinçer et Serhat Yüksel. « Neuro quantum-inspired decision-making for investor perception in green and conventional bond investments ». Investment Management and Financial Innovations 21, n^o 1 (9 février 2024) : 168–84. http://dx.doi.org/10.21511/imfi.21(1).2024.14.

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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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Harkhoe, Krishan, Guy Verschaffelt et Guy Van der Sande. « Neuro-Inspired Computing with Spin-VCSELs ». Applied Sciences 11, n^o 9 (7 mai 2021) : 4232. http://dx.doi.org/10.3390/app11094232.

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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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Zhong, Xiaopin, et Lin Ma. « A Neuro-inspired Adaptive Motion Detector ». Optics and Photonics Journal 03, n^o 02 (2013) : 94–98. http://dx.doi.org/10.4236/opj.2013.32b024.

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Huang, Ping-Chen, et Jan M. Rabaey. « A Neuro-Inspired Spike Pattern Classifier ». IEEE Journal on Emerging and Selected Topics in Circuits and Systems 8, n^o 3 (septembre 2018) : 555–65. http://dx.doi.org/10.1109/jetcas.2018.2842035.

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Kahol, Kanav, et Sethuraman Panchanathan. « Neuro-cognitively inspired haptic user interfaces ». Multimedia Tools and Applications 37, n^o 1 (6 septembre 2007) : 15–38. http://dx.doi.org/10.1007/s11042-007-0167-y.

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GINGL, ZOLTAN, LASZLO B. KISH et SUNIL P. KHATRI. « TOWARDS BRAIN-INSPIRED COMPUTING ». Fluctuation and Noise Letters 09, n^o 04 (décembre 2010) : 403–12. http://dx.doi.org/10.1142/s0219477510000332.

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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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Blachowicz, Tomasz, Jacek Grzybowski, Pawel Steblinski et Andrea Ehrmann. « Neuro-Inspired Signal Processing in Ferromagnetic Nanofibers ». Biomimetics 6, n^o 2 (26 mai 2021) : 32. http://dx.doi.org/10.3390/biomimetics6020032.

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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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Yu, Shimeng. « Neuro-Inspired Computing With Emerging Nonvolatile Memorys ». Proceedings of the IEEE 106, n^o 2 (février 2018) : 260–85. http://dx.doi.org/10.1109/jproc.2018.2790840.

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Dumitrache, Ioan, Simona Iuliana Caramihai, Mihnea Alexandru Moisescu et Ioan Stefan Sacala. « Neuro-inspired Framework for cognitive manufacturing control ». IFAC-PapersOnLine 52, n^o 13 (2019) : 910–15. http://dx.doi.org/10.1016/j.ifacol.2019.11.311.

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Rezk, Karen, et 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, n^o 3 (15 juillet 2016) : 221–27. http://dx.doi.org/10.5737/23688076263221227.

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Henniquau, Dimitri, Pierre Falez, Philippe Devienne, Christel Vanbesien Mailliot, Alexis Vlandas, Alain Cappy et Virginie Hoel. « 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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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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Marco-Detchart, Cedric, Giancarlo Lucca, Carlos Lopez-Molina, Laura De Miguel, Graçaliz Pereira Dimuro et Humberto Bustince. « Neuro-inspired edge feature fusion using Choquet integrals ». Information Sciences 581 (décembre 2021) : 740–54. http://dx.doi.org/10.1016/j.ins.2021.10.016.

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Wang, Panni, et Shimeng Yu. « Ferroelectric devices and circuits for neuro-inspired computing ». MRS Communications 10, n^o 4 (21 septembre 2020) : 538–48. http://dx.doi.org/10.1557/mrc.2020.71.

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Shi, Yuanhong, Qilin Hua, Zilong Dong, Bingjun Wang, Xinhuan Dai, Jianan Niu, Zhaowei Cui, Tianci Huang, Zhong Lin Wang et Weiguo Hu. « Neuro-inspired thermoresponsive nociceptor for intelligent sensory systems ». Nano Energy 113 (août 2023) : 108549. http://dx.doi.org/10.1016/j.nanoen.2023.108549.

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He, Yongli, Yixin Zhu et Qing Wan. « Oxide Ionic Neuro-Transistors for Bio-inspired Computing ». Nanomaterials 14, n^o 7 (27 mars 2024) : 584. http://dx.doi.org/10.3390/nano14070584.

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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.

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Liu, Shuang, Guangyao Wang, Tianshuo Bai, Kefan Mo, Jiaqi Chen, Wanru Mao, Wenjia Wang, Zihan Yuan et Biao Pan. « Magnetic Skyrmion-Based Spiking Neural Network for Pattern Recognition ». Applied Sciences 12, n^o 19 (27 septembre 2022) : 9698. http://dx.doi.org/10.3390/app12199698.

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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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Wang, Qiang, Gang Niu, Wei Ren, Ruobing Wang, Xiaogang Chen, Xi Li, Zuo‐Guang Ye, Ya‐Hong Xie, Sannian Song et Zhitang Song. « Phase Change Random Access Memory for Neuro‐Inspired Computing ». Advanced Electronic Materials 7, n^o 6 (17 mars 2021) : 2001241. http://dx.doi.org/10.1002/aelm.202001241.

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Kuzum, Duygu. « Neuro-Inspired Computing with Resistive Switching Devices [Guest Editorial] ». IEEE Nanotechnology Magazine 12, n^o 3 (septembre 2018) : 4. http://dx.doi.org/10.1109/mnano.2018.2849799.

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Chabi, Djaafar, Damien Querlioz, Weisheng Zhao et Jacques-Olivier Klein. « Robust learning approach for neuro-inspired nanoscale crossbar architecture ». ACM Journal on Emerging Technologies in Computing Systems 10, n^o 1 (janvier 2014) : 1–20. http://dx.doi.org/10.1145/2539123.

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Shoureshi, Rahmat A., Tracy Schantz et Sun W. Lim. « Bio-inspired neuro-symbolic approach to diagnostics of structures ». Smart Structures and Systems 7, n^o 3 (25 mars 2011) : 229–40. http://dx.doi.org/10.12989/sss.2011.7.3.229.

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

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Farquhar, E., et P. Hasler. « A bio-physically inspired silicon neuron ». IEEE Transactions on Circuits and Systems I : Regular Papers 52, n^o 3 (mars 2005) : 477–88. http://dx.doi.org/10.1109/tcsi.2004.842871.

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HAMMAD, ABDALLAH, SIMON X. YANG, M. TAREK ELEWA, HALA MANSOUR et 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, n^o 03 (septembre 2011) : 357–75. http://dx.doi.org/10.1142/s1469026811003148.

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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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Mozaffari, Ahmad, Alireza Fathi et 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, n^o 1 (4 mars 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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Dominguez-Morales, Manuel, Juan P. Domínguez-Morales, Ángel Jiménez-Fernández, Alejandro Linares-Barranco et Gabriel Jiménez-Moreno. « Stereo Matching in Address-Event-Representation (AER) Bio-Inspired Binocular Systems in a Field-Programmable Gate Array (FPGA) ». Electronics 8, n^o 4 (8 avril 2019) : 410. http://dx.doi.org/10.3390/electronics8040410.

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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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Adetomi, Adewale, Mohsin Raza, Khubaib Ahmed, Tughrul Arslan, Amir Hussain et Ahsan Adeel. « Towards two-point neuron-driven energy-efficient multimodal open master hearing aid ». Journal of the Acoustical Society of America 154, n^o 4_supplement (1 octobre 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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Hafsi, Bilel, Rabii Elmissaoui et Adel Kalboussi. « Neural Network Based on SET Inverter Structures : Neuro-Inspired Memory ». World Journal of Nano Science and Engineering 04, n^o 04 (2014) : 134–42. http://dx.doi.org/10.4236/wjnse.2014.44017.

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Mahmoudi, Maryam Tayefeh, Fattaneh Taghiyareh et Babak N. Araabi. « A neuro-fuzzy immune inspired classifier for task-oriented texts ». Journal of Intelligent & ; Fuzzy Systems 25, n^o 3 (2013) : 673–83. http://dx.doi.org/10.3233/ifs-120674.

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Hamilton, Tara Julia, Saeed Afshar, Andre van Schaik et Jonathan Tapson. « Stochastic Electronics : A Neuro-Inspired Design Paradigm for Integrated Circuits ». Proceedings of the IEEE 102, n^o 5 (mai 2014) : 843–59. http://dx.doi.org/10.1109/jproc.2014.2310713.

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Corchado, E., et M. Wozniak. « Editorial : Neuro-symbolic Algorithms and Models for Bio-inspired Systems ». Logic Journal of IGPL 19, n^o 2 (8 juillet 2010) : 289–92. http://dx.doi.org/10.1093/jigpal/jzq026.

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Galluccio, Laura, Sergio Palazzo et G. Enrico Santagati. « Characterization of molecular communications among implantable biomedical neuro-inspired nanodevices ». Nano Communication Networks 4, n^o 2 (juin 2013) : 53–64. http://dx.doi.org/10.1016/j.nancom.2013.03.001.

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Tang, Huajin, Rui Yan et Kay Chen Tan. « Cognitive Navigation by Neuro-Inspired Localization, Mapping, and Episodic Memory ». IEEE Transactions on Cognitive and Developmental Systems 10, n^o 3 (septembre 2018) : 751–61. http://dx.doi.org/10.1109/tcds.2017.2776965.

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

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Zhang, Wenbin, Peng Yao, Bin Gao, Qi Liu, Dong Wu, Qingtian Zhang, Yuankun Li et al. « Edge learning using a fully integrated neuro-inspired memristor chip ». Science 381, n^o 6663 (15 septembre 2023) : 1205–11. http://dx.doi.org/10.1126/science.ade3483.

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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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Soures, Nicholas, Vedant Karia et Dhireesha Kudithipudi. « Advancing Neuro-Inspired Lifelong Learning for Edge with Co-Design ». Proceedings of the AAAI Symposium Series 3, n^o 1 (20 mai 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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Krestinskaya, O., et A. P. James. « Analogue neuro-memristive convolutional dropout nets ». Proceedings of the Royal Society A : Mathematical, Physical and Engineering Sciences 476, n^o 2242 (octobre 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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Feldhoff, Frank, et Hannes Toepfer. « Niobium Neuron : RSFQ Based Bio-Inspired Circuit ». IEEE Transactions on Applied Superconductivity 31, n^o 5 (août 2021) : 1–5. http://dx.doi.org/10.1109/tasc.2021.3063212.

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Wang, Yuwei, Yi Zeng, Jianbo Tang et Bo Xu. « Biological Neuron Coding Inspired Binary Word Embeddings ». Cognitive Computation 11, n^o 5 (24 juillet 2019) : 676–84. http://dx.doi.org/10.1007/s12559-019-09643-1.

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Ding, Keyuan, Jiangjing Wang, Yuxing Zhou, He Tian, Lu Lu, Riccardo Mazzarello, Chunlin Jia, Wei Zhang, Feng Rao et Evan Ma. « Phase-change heterostructure enables ultralow noise and drift for memory operation ». Science 366, n^o 6462 (22 août 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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Pruthi, Dimple, et Rashmi Bhardwaj. « Modeling air quality index using optimized neuronal networks inspired by swarms ». Environmental Engineering Research 26, n^o 6 (27 novembre 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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Djahafi, Fatiha, et Abdelkader Gafour. « Neuro-Immune Model Based on Bio-Inspired Methods for Medical Diagnosis ». International Journal of Ambient Computing and Intelligence 13, n^o 1 (janvier 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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Luo, Yuan-Chun, Jae Hur et 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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Susi, Gianluca, Simone Acciarito, Teodoro Pascual, Alessandro Cristini et Fernando Maestú. « Towards Neuro-Inspired Electronic Oscillators Based on The Dynamical Relaying Mechanism ». International Journal on Advanced Science, Engineering and Information Technology 9, n^o 2 (12 avril 2019) : 569. http://dx.doi.org/10.18517/ijaseit.9.2.8347.

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Wang, Fu-Cheng, Yu-You Lin, You-Chi Li, Po-Yin Chen et Chung-Huang Yu. « Development of an Automated Assistive Trainer Inspired by Neuro-developmental Treatment ». Sensors and Materials 32, n^o 9 (30 septembre 2020) : 3019. http://dx.doi.org/10.18494/sam.2020.2708.

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Lee, Wang Wei, Yu Jun Tan, Haicheng Yao, Si Li, Hian Hian See, Matthew Hon, Kian Ann Ng, Betty Xiong, John S. Ho et Benjamin C. K. Tee. « A neuro-inspired artificial peripheral nervous system for scalable electronic skins ». Science Robotics 4, n^o 32 (17 juillet 2019) : eaax2198. http://dx.doi.org/10.1126/scirobotics.aax2198.

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The human sense of touch is essential for dexterous tool usage, spatial awareness, and social communication. Equipping intelligent human-like androids and prosthetics with electronic skins—a large array of sensors spatially distributed and capable of rapid somatosensory perception—will enable them to work collaboratively and naturally with humans to manipulate objects in unstructured living environments. Previously reported tactile-sensitive electronic skins largely transmit the tactile information from sensors serially, resulting in readout latency bottlenecks and complex wiring as the number of sensors increases. Here, we introduce the Asynchronously Coded Electronic Skin (ACES)—a neuromimetic architecture that enables simultaneous transmission of thermotactile information while maintaining exceptionally low readout latencies, even with array sizes beyond 10,000 sensors. We demonstrate prototype arrays of up to 240 artificial mechanoreceptors that transmitted events asynchronously at a constant latency of 1 ms while maintaining an ultra-high temporal precision of <60 ns, thus resolving fine spatiotemporal features necessary for rapid tactile perception. Our platform requires only a single electrical conductor for signal propagation, realizing sensor arrays that are dynamically reconfigurable and robust to damage. We anticipate that the ACES platform can be integrated with a wide range of skin-like sensors for artificial intelligence (AI)–enhanced autonomous robots, neuroprosthetics, and neuromorphic computing hardware for dexterous object manipulation and somatosensory perception.

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Fellous, Jean-Marc, Peter Dominey et Alfredo Weitzenfeld. « Complex spatial navigation in animals, computational models and neuro-inspired robots ». Biological Cybernetics 114, n^o 2 (avril 2020) : 137–38. http://dx.doi.org/10.1007/s00422-020-00832-y.

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Nebti, Salima, et Abdellah Boukerram. « Handwritten characters recognition based on nature-inspired computing and neuro-evolution ». Applied Intelligence 38, n^o 2 (21 juin 2012) : 146–59. http://dx.doi.org/10.1007/s10489-012-0362-z.

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Pagkalos, Michalis, Roman Makarov et Panayiota Poirazi. « Leveraging dendritic properties to advance machine learning and neuro-inspired computing ». Current Opinion in Neurobiology 85 (avril 2024) : 102853. http://dx.doi.org/10.1016/j.conb.2024.102853.

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