Artykuły w czasopismach: „Neuro inspired”

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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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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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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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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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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, 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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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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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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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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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, 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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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, no. 2 (2018): 260–85. http://dx.doi.org/10.1109/jproc.2018.2790840.

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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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, 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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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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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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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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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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Susi, Gianluca, Simone Acciarito, Teodoro Pascual, Alessandro Cristini, and Fernando Maestú. "Towards Neuro-Inspired Electronic Oscillators Based on The Dynamical Relaying Mechanism." International Journal on Advanced Science, Engineering and Information Technology 9, no. 2 (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, and Chung-Huang Yu. "Development of an Automated Assistive Trainer Inspired by Neuro-developmental Treatment." Sensors and Materials 32, no. 9 (2020): 3019. http://dx.doi.org/10.18494/sam.2020.2708.

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Lee, Wang Wei, Yu Jun Tan, Haicheng Yao, et al. "A neuro-inspired artificial peripheral nervous system for scalable electronic skins." Science Robotics 4, no. 32 (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, and Alfredo Weitzenfeld. "Complex spatial navigation in animals, computational models and neuro-inspired robots." Biological Cybernetics 114, no. 2 (2020): 137–38. http://dx.doi.org/10.1007/s00422-020-00832-y.

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Nebti, Salima, and Abdellah Boukerram. "Handwritten characters recognition based on nature-inspired computing and neuro-evolution." Applied Intelligence 38, no. 2 (2012): 146–59. http://dx.doi.org/10.1007/s10489-012-0362-z.

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

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Prashanth, Basutkar Umamaheshwar Venkata, and Mohammed Riyaz Ahmed. "Design and Implementation of Reconfigurable Neuro-Inspired Computing Model on a FPGA." Advances in Science, Technology and Engineering Systems Journal 5, no. 5 (2020): 332–41. http://dx.doi.org/10.25046/aj050541.

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Gorzalczany, Marian B., and Zdzislaw Piasta. "Neuro-fuzzy approach versus rough-set inspired methodology for intelligent decision support." Information Sciences 120, no. 1-4 (1999): 45–68. http://dx.doi.org/10.1016/s0020-0255(99)00070-5.

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Bennett, Christopher H., Jean-Etienne Lorival, Francois Marc, et al. "Multiscaled Simulation Methodology for Neuro-Inspired Circuits Demonstrated with an Organic Memristor." IEEE Transactions on Multi-Scale Computing Systems 4, no. 4 (2018): 822–32. http://dx.doi.org/10.1109/tmscs.2017.2773523.

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Ng, G. S., F. Liu, T. F. Loh, and C. Quek. "A novel brain-inspired neuro-fuzzy hybrid system for artificial ventilation modeling." Expert Systems with Applications 39, no. 15 (2012): 11808–17. http://dx.doi.org/10.1016/j.eswa.2012.01.028.

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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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Yousefi, Bardia, and Chu Kiong Loo. "Biologically-Inspired Computational Neural Mechanism for Human Action/activity Recognition: A Review." Electronics 8, no. 10 (2019): 1169. http://dx.doi.org/10.3390/electronics8101169.

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Theoretical neuroscience investigation shows valuable information on the mechanism for recognizing the biological movements in the mammalian visual system. This involves many different fields of researches such as psychological, neurophysiology, neuro-psychological, computer vision, and artificial intelligence (AI). The research on these areas provided massive information and plausible computational models. Here, a review on this subject is presented. This paper describes different perspective to look at this task including action perception, computational and knowledge based modeling, psychological, and neuroscience approaches.

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Arena, Paolo, Luca Patanè, and Salvatore Taffara. "Energy Efficiency of a Quadruped Robot with Neuro-Inspired Control in Complex Environments." Energies 14, no. 2 (2021): 433. http://dx.doi.org/10.3390/en14020433.

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This paper proposes an analysis of the energy efficiency of a small quadruped robotic structure, designed based on the MIT Mini Cheetah, controlled using a central pattern generator based on the FitzHugh–Nagumo neuron. The robot’s performance evaluated on structurally complex terrain in a dynamic simulation environment is compared with other robotic structures on wheels and with hybrid architectures. The energy cost involved in carrying out an assigned task involving the need to traverse uneven terrain is calculated as a relevant index to be taken into account. In particular, simple control strategies impacting the leg trajectories are taken into account as the main factors affecting the energy efficiency in different terrain configurations. The adaptation of the leg trajectories is evaluated depending on the terrain characteristics, improving the locomotion performance.

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Kuncic, Zdenka, and Tomonobu Nakayama. "Neuromorphic nanowire networks: principles, progress and future prospects for neuro-inspired information processing." Advances in Physics: X 6, no. 1 (2021): 1894234. http://dx.doi.org/10.1080/23746149.2021.1894234.

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Munawar, Asim. "How a shopping mall trip inspired me to work in neuro-symbolic AI." Communications of the ACM 65, no. 5 (2022): 11. http://dx.doi.org/10.1145/3528571.

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Ymaji, Yuuki, Eisaku Horiguchi, and Hirotsugu OKUNO. "FPGA implementation of a neuro-inspired algorithm for spatio-temporal visual feature extraction." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2021 (2021): 2P2—H08. http://dx.doi.org/10.1299/jsmermd.2021.2p2-h08.

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