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Journal articles on the topic 'Neuro inspiré'

Author: Grafiati

Published: 8 June 2024

Last updated: 31 July 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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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 implemente

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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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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 implemente

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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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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, liqui

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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 interva

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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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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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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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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 b

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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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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

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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 coup

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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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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 m

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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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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, whi

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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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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 skyrmio

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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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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

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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 determini

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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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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 inform

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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

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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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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

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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 consoli

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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 optimizati

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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

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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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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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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 t

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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 sec

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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-inspire

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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 accu

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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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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

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