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Journal articles on the topic 'Neuromorphic technologies'

Author: Grafiati

Published: 15 June 2024

Last updated: 31 July 2025

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1

Okazaki, Atsuya. "Hardware Technologies for Neuromorphic Computing." Journal of the Robotics Society of Japan 35, no. 3 (2017): 209–14. http://dx.doi.org/10.7210/jrsj.35.209.

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Argyris, Apostolos. "Photonic neuromorphic technologies in optical communications." Nanophotonics 11, no. 5 (2022): 897–916. http://dx.doi.org/10.1515/nanoph-2021-0578.

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Abstract Machine learning (ML) and neuromorphic computing have been enforcing problem-solving in many applications. Such approaches found fertile ground in optical communications, a technological field that is very demanding in terms of computational speed and complexity. The latest breakthroughs are strongly supported by advanced signal processing, implemented in the digital domain. Algorithms of different levels of complexity aim at improving data recovery, expanding the reach of transmission, validating the integrity of the optical network operation, and monitoring data transfer faults. Lat

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Vianello, Elisa, and Melika Payvand. "Scaling neuromorphic systems with 3D technologies." Nature Electronics 7, no. 6 (2024): 419–21. http://dx.doi.org/10.1038/s41928-024-01188-y.

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Kim, Chul-Heung, Suhwan Lim, Sung Yun Woo, et al. "Emerging memory technologies for neuromorphic computing." Nanotechnology 30, no. 3 (2018): 032001. http://dx.doi.org/10.1088/1361-6528/aae975.

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Varshika, M. Lakshmi, Federico Corradi, and Anup Das. "Nonvolatile Memories in Spiking Neural Network Architectures: Current and Emerging Trends." Electronics 11, no. 10 (2022): 1610. http://dx.doi.org/10.3390/electronics11101610.

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A sustainable computing scenario demands more energy-efficient processors. Neuromorphic systems mimic biological functions by employing spiking neural networks for achieving brain-like efficiency, speed, adaptability, and intelligence. Current trends in neuromorphic technologies address the challenges of investigating novel materials, systems, and architectures for enabling high-integration and extreme low-power brain-inspired computing. This review collects the most recent trends in exploiting the physical properties of nonvolatile memory technologies for implementing efficient in-memory and

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Della Rocca, Mattia. "Of the Artistic Nude and Technological Behaviorism." Nuncius 32, no. 2 (2017): 376–411. http://dx.doi.org/10.1163/18253911-03202006.

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Neuromorphic technologies lie at the core of 21st century neuroscience, especially in the “big brain science” projects started in 2013 – i.e. the BRAIN Initiative and the Human Brain Project. While neuromorphism and the “reverse engineering” of the brain are often presented as a “methodological revolution” in the brain sciences, these concepts have a long history which is strongly interconnected with the developments in neuroscience and the related field of bioengineering since the end of World War II. In this paper I provide a short review of the first generation of “neuromorphic devices” cre

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Rajendran, Bipin, and Fabien Alibart. "Neuromorphic Computing Based on Emerging Memory Technologies." IEEE Journal on Emerging and Selected Topics in Circuits and Systems 6, no. 2 (2016): 198–211. http://dx.doi.org/10.1109/jetcas.2016.2533298.

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Woo, Jiyong, Jeong Hun Kim, Jong‐Pil Im, and Seung Eon Moon. "Recent Advancements in Emerging Neuromorphic Device Technologies." Advanced Intelligent Systems 2, no. 10 (2020): 2000111. http://dx.doi.org/10.1002/aisy.202000111.

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Woo, Jiyong, Jeong Hun Kim, Jong‐Pil Im, and Seung Eon Moon. "Recent Advancements in Emerging Neuromorphic Device Technologies." Advanced Intelligent Systems 2, no. 10 (2020): 2070101. http://dx.doi.org/10.1002/aisy.202070101.

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Kurshan, Eren, Hai Li, Mingoo Seok, and Yuan Xie. "A Case for 3D Integrated System Design for Neuromorphic Computing and AI Applications." International Journal of Semantic Computing 14, no. 04 (2020): 457–75. http://dx.doi.org/10.1142/s1793351x20500063.

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Over the last decade, artificial intelligence (AI) has found many applications areas in the society. As AI solutions have become more sophistication and the use cases grew, they highlighted the need to address performance and energy efficiency challenges faced during the implementation process. To address these challenges, there has been growing interest in neuromorphic chips. Neuromorphic computing relies on non von Neumann architectures as well as novel devices, circuits and manufacturing technologies to mimic the human brain. Among such technologies, three-dimensional (3D) integration is an

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Park, Jeongwon. "(Invited) Perspectives and Opportunities for Neuromorphic Computing and Engineering." ECS Meeting Abstracts MA2025-01, no. 63 (2025): 3081. https://doi.org/10.1149/ma2025-01633081mtgabs.

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Neuromorphic computing is set to influence the future of computing significantly. While much research has focused on hardware improvements, this presentation highlights recent advancements in neuromorphic computing and its applications. We underscore the attributes that make neuromorphic technologies attractive for future computing requirements and examine opportunities for further algorithm and application development within these systems. As Moore's law approaches its limits and Dennard scaling concludes, the computing community actively seeks new technologies to maintain performance growth.

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Orii, Yasumitsu, Akihiro Horibe, Kuniaki Sueoka, et al. "PERSPECTIVE ON REQUIRED PACKAGING TECHNOLOGIES FOR NEUROMORPHIC DEVICES." International Symposium on Microelectronics 2015, no. 1 (2015): 000561–66. http://dx.doi.org/10.4071/isom-2015-tha15.

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Cognitive computing has capability of machine learning, recognition and proposal. It is essential to make human life richer, more productive and more intelligent. For the realization of the cognitive computing, an efficient and scalable non-von Neumann architecture inspired by the human brain structure has been developed and a device which demonstrates the concept was also built. This device mimics the signal processing of the human brain, packing one million neuron circuits in 4,096 cores. It consumes almost 1,000 times less energy per event compared with a state-of-the-art multiprocessor. Ho

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Li, Sheng, Lin Gao, Changjian Liu, Haihong Guo, and Junsheng Yu. "Biomimetic Neuromorphic Sensory System via Electrolyte Gated Transistors." Sensors 24, no. 15 (2024): 4915. http://dx.doi.org/10.3390/s24154915.

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Biomimetic neuromorphic sensing systems, inspired by the structure and function of biological neural networks, represent a major advancement in the field of sensing technology and artificial intelligence. This review paper focuses on the development and application of electrolyte gated transistors (EGTs) as the core components (synapses and neuros) of these neuromorphic systems. EGTs offer unique advantages, including low operating voltage, high transconductance, and biocompatibility, making them ideal for integrating with sensors, interfacing with biological tissues, and mimicking neural proc

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Tyler, Neil. "Tempo Targets Low-Power Chips for AI Applications." New Electronics 52, no. 13 (2019): 7. http://dx.doi.org/10.12968/s0047-9624(22)61557-8.

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Elfighi, Melad Mohamed Salim. "Advancements and Challenges in Neuromorphic Computing: Bridging Neuroscience and Artificial Intelligence." International Journal for Research in Applied Science and Engineering Technology 13, no. 1 (2025): 627–32. https://doi.org/10.22214/ijraset.2025.66411.

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Neuromorphic computing represents a paradigm shift in computational design, aiming to emulate the neural structures and functionalities of the human brain. This approach seeks to enhance efficiency and adaptability in artificial intelligence (AI) systems. This paper provides a comprehensive review of recent advancements in neuromorphic hardware and software, highlighting their potential to revolutionize AI by enabling real-time processing and energy-efficient computations. Additionally, it examines the challenges inherent in replicating complex neural processes, including issues related to sca

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Pammi, Venkata Anirudh, and Sylvain Barbay. "Micro-lasers for neuromorphic computing." Photoniques, no. 104 (September 2020): 26–29. http://dx.doi.org/10.1051/photon/202010426.

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Spiking micro-lasers are interesting neuromorphic building blocks to realize all-optical spiking neural networks. Optical spike-based computing offers speed and parallelism of optical technologies combined with a sparse way of representing information in spikes, thus with a potential for efficient brain-inspired computing. This article reviews some of the latest advances in this field using single and coupled semiconductor excitable micro-lasers.

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Vanarse, Anup, Adam Osseiran, and Alexander Rassau. "Neuromorphic engineering — A paradigm shift for future IM technologies." IEEE Instrumentation & Measurement Magazine 22, no. 2 (2019): 4–9. http://dx.doi.org/10.1109/mim.2019.8674627.

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Park, Jaeseoung. "Bulk Switching Trilayer Metal Oxide RRAM for Neuromorphic Computing at the Edge." Ceramist 28, no. 1 (2025): 106–16. https://doi.org/10.31613/ceramist.2025.00024.

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The Complementary metal oxide semiconductor-Resistive random access memory (RRAM) integration presents significant potential for energy-efficient and high-speed neuromorphic computing. However, conventional filamentary RRAM technologies that rely on filamentary switching face challenges such as high variability, noise, reduced computational accuracy, and increased energy consumption. To address these limitations, we developed a filament-free, bulk-switching RRAM technology. By designing a trilayer metal-oxide stack, we optimized switching behavior across different oxide thicknesses and oxygen

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Schneider, Michael, Emily Toomey, Graham Rowlands, Jeff Shainline, Paul Tschirhart, and Ken Segall. "SuperMind: a survey of the potential of superconducting electronics for neuromorphic computing." Superconductor Science and Technology 35, no. 5 (2022): 053001. http://dx.doi.org/10.1088/1361-6668/ac4cd2.

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Abstract Neuromorphic computing is a broad field that uses biological inspiration to address computing design. It is being pursued in many hardware technologies, both novel and conventional. We discuss the use of superconductive electronics for neuromorphic computing and why they are a compelling technology for the design of neuromorphic computing systems. One example is the natural spiking behavior of Josephson junctions and the ability to transmit short voltage spikes without the resistive capacitive time constants that typically hinder spike-based computing. We review the work that has been

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Bisquert, Juan. "Recent advances in fluidic neuromorphic computing." Applied Physics Reviews 12, no. 021309 (2025): 1–30. https://doi.org/10.1063/5.0235267.

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Human brain is capable of optimizing information flow and processing without energy-intensive data shuttling between processor andmemory. At the core of this unique capability are billions of neurons connected through trillions of synapses—basic processing units ofthe brain. The action potentials or “spikes” based temporal processing using the regulated flow of ions across ion channels in neuron cellsallows sparse and efficient transmission of data in the brain. Emerging systems based on confined fluidic systems have provided a frameworkfor a new type of neuromorphic computin

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Diao, Yu, Yaoxuan Zhang, Yanran Li, and Jie Jiang. "Metal-Oxide Heterojunction: From Material Process to Neuromorphic Applications." Sensors 23, no. 24 (2023): 9779. http://dx.doi.org/10.3390/s23249779.

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As technologies like the Internet, artificial intelligence, and big data evolve at a rapid pace, computer architecture is transitioning from compute-intensive to memory-intensive. However, traditional von Neumann architectures encounter bottlenecks in addressing modern computational challenges. The emulation of the behaviors of a synapse at the device level by ionic/electronic devices has shown promising potential in future neural-inspired and compact artificial intelligence systems. To address these issues, this review thoroughly investigates the recent progress in metal-oxide heterostructure

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Meng, Xiaohan, Runsheng Gao, Xiaojian Zhu, and Run-Wei Li. "Ion-modulation optoelectronic neuromorphic devices: mechanisms, characteristics, and applications." Journal of Semiconductors 46, no. 2 (2025): 021402. https://doi.org/10.1088/1674-4926/24100025.

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Abstract The traditional von Neumann architecture faces inherent limitations due to the separation of memory and computation, leading to high energy consumption, significant latency, and reduced operational efficiency. Neuromorphic computing, inspired by the architecture of the human brain, offers a promising alternative by integrating memory and computational functions, enabling parallel, high-speed, and energy-efficient information processing. Among various neuromorphic technologies, ion-modulated optoelectronic devices have garnered attention due to their excellent ionic tunability and the

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Milo, Valerio, Gerardo Malavena, Christian Monzio Compagnoni, and Daniele Ielmini. "Memristive and CMOS Devices for Neuromorphic Computing." Materials 13, no. 1 (2020): 166. http://dx.doi.org/10.3390/ma13010166.

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Neuromorphic computing has emerged as one of the most promising paradigms to overcome the limitations of von Neumann architecture of conventional digital processors. The aim of neuromorphic computing is to faithfully reproduce the computing processes in the human brain, thus paralleling its outstanding energy efficiency and compactness. Toward this goal, however, some major challenges have to be faced. Since the brain processes information by high-density neural networks with ultra-low power consumption, novel device concepts combining high scalability, low-power operation, and advanced comput

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Vinuesa, Guillermo, Hector Garcia, Salvador Duenas, and Helena Castan. "(Invited) Thermoelectric Analysis of Dielectric Materials Properties for Neuromorphic Technologies." ECS Meeting Abstracts MA2024-01, no. 21 (2024): 1294. http://dx.doi.org/10.1149/ma2024-01211294mtgabs.

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The effort made in recent years in the development of new memories has led to a significant advance in emerging technologies, which have proven their usefulness not only in the field of memories, but also to obtain devices that perform an artificial synapse and thus emulate biological neurons. Among the novel concepts, resistive-switching random access memory (RRAM), or memristive device, has attracted a great deal of interest for its ability to store multiple states. In fact, its operation is based on the so-called resistive switching, which consists of the formation of conductive filaments t

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Chakraborty, I., A. Jaiswal, A. K. Saha, S. K. Gupta, and K. Roy. "Pathways to efficient neuromorphic computing with non-volatile memory technologies." Applied Physics Reviews 7, no. 2 (2020): 021308. http://dx.doi.org/10.1063/1.5113536.

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Covi, Erika, Halid Mulaosmanovic, Benjamin Max, Stefan Slesazeck, and Thomas Mikolajick. "Ferroelectric-based synapses and neurons for neuromorphic computing." Neuromorphic Computing and Engineering 2, no. 1 (2022): 012002. http://dx.doi.org/10.1088/2634-4386/ac4918.

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Abstract The shift towards a distributed computing paradigm, where multiple systems acquire and elaborate data in real-time, leads to challenges that must be met. In particular, it is becoming increasingly essential to compute on the edge of the network, close to the sensor collecting data. The requirements of a system operating on the edge are very tight: power efficiency, low area occupation, fast response times, and on-line learning. Brain-inspired architectures such as spiking neural networks (SNNs) use artificial neurons and synapses that simultaneously perform low-latency computation and

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Rajeev, Borra. "Neuromorphic Computing: Bridging Biological Intelligence and Artificial Intelligence." International Journal of Engineering and Advanced Technology (IJEAT) 14, no. 2 (2024): 19–24. https://doi.org/10.35940/ijeat.B4558.14021224.

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<strong>Abstract:</strong> Neuromorphic computing represents a groundbreaking paradigm shift in the realm of artificial intelligence, aiming to replicate the architecture and operational mechanisms of the human brain. This paper provides a comprehensive exploration of the foundational principles that underpin this innovative approach, examining the technological implementations that are driving advancements in the field. We delve into a diverse array of applications across various sectors, highlighting the versatility and relevance of neuromorphic systems. Key challenges such as scalability, i

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Allwood, Dan A., Matthew O. A. Ellis, David Griffin, et al. "A perspective on physical reservoir computing with nanomagnetic devices." Applied Physics Letters 122, no. 4 (2023): 040501. http://dx.doi.org/10.1063/5.0119040.

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Neural networks have revolutionized the area of artificial intelligence and introduced transformative applications to almost every scientific field and industry. However, this success comes at a great price; the energy requirements for training advanced models are unsustainable. One promising way to address this pressing issue is by developing low-energy neuromorphic hardware that directly supports the algorithm's requirements. The intrinsic non-volatility, non-linearity, and memory of spintronic devices make them appealing candidates for neuromorphic devices. Here, we focus on the reservoir c

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Borra, Rajeev. "Neuromorphic Computing: Bridging Biological Intelligence and Artificial Intelligence." International Journal of Engineering and Advanced Technology 14, no. 2 (2024): 19–24. https://doi.org/10.35940/ijeat.b4558.14021224.

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Neuromorphic computing represents a groundbreaking paradigm shift in the realm of artificial intelligence, aiming to replicate the architecture and operational mechanisms of the human brain. This paper provides a comprehensive exploration of the foundational principles that underpin this innovative approach, examining the technological implementations that are driving advancements in the field. We delve into a diverse array of applications across various sectors, highlighting the versatility and relevance of neuromorphic systems. Key challenges such as scalability, integration with existing te

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Abbas, Haider, Jiayi Li, and Diing Shenp Ang. "Conductive Bridge Random Access Memory (CBRAM): Challenges and Opportunities for Memory and Neuromorphic Computing Applications." Micromachines 13, no. 5 (2022): 725. http://dx.doi.org/10.3390/mi13050725.

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Due to a rapid increase in the amount of data, there is a huge demand for the development of new memory technologies as well as emerging computing systems for high-density memory storage and efficient computing. As the conventional transistor-based storage devices and computing systems are approaching their scaling and technical limits, extensive research on emerging technologies is becoming more and more important. Among other emerging technologies, CBRAM offers excellent opportunities for future memory and neuromorphic computing applications. The principles of the CBRAM are explored in depth

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Shen, Yuxiang. "Computer Vision: Technologies and Applications." Applied and Computational Engineering 163, no. 1 (2025): 35–41. https://doi.org/10.54254/2755-2721/2025.23817.

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Computer vision, as a crucial branch of artificial intelligence, is profoundly transforming various aspects of human society. This paper provides a systematic exploration of the key technologies, application domains, challenges, and future development trends in computer vision. We begin with a detailed analysis of core technologies including convolutional neural networks, Transformer architectures, and edge computing. Subsequently, we conduct an in-depth investigation of innovative applications in healthcare, autonomous driving, smart agriculture, and security surveillance. Furthermore, we exa

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Diaz-Parra, Ocotlán, Francisco R. Trejo-Macotela, Jorge A. Ruiz-Vanoye, et al. "Integrated Biomimetics: Natural Innovations for Urban Design, Smart Technologies, and Human Health." Applied Sciences 15, no. 13 (2025): 7323. https://doi.org/10.3390/app15137323.

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Biomimetics has emerged as a transformative interdisciplinary approach that harnesses nature’s evolutionary strategies to develop sustainable solutions across diverse fields. This study explores its integrative role in shaping smart cities, advancing artificial intelligence and robotics, innovating biomedical applications, and enhancing computational design tools. By analysing the evolution of biomimetic principles and their technological impact, this work highlights how nature-inspired solutions contribute to energy efficiency, adaptive urban planning, bioengineered materials, and intelligent

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K P, VISHNUPRIYA, JWALA JOSE, PRINCE JOY, SRITHA S, and GIBI K. S. "Brain-Inspired Artificial Intelligence: Revolutionizing Computing and Cognitive Systems." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–8. https://doi.org/10.55041/ijsrem39825.

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Brain-inspired artificial intelligence (AI) is a rapidly evolving field that seeks to model computational systems after the structure, processes, and functioning of the human brain. By drawing from neuroscience and cognitive science, brain-inspired AI aims to improve the efficiency, scalability, and adaptability of machine learning algorithms. This paper explores the key technologies and advancements in the realm of brain-inspired AI, including neural networks, neuromorphic hardware, brain-computer interfaces, and algorithms inspired by biological learning mechanisms. Additionally, we will ana

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Huang, Tianci, Yuxuan Wang, Zhihan Jin, et al. "A Review of Nanowire Devices Applied in Simulating Neuromorphic Computing." Nanomaterials 15, no. 10 (2025): 724. https://doi.org/10.3390/nano15100724.

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With the rapid advancement of artificial intelligence and machine learning technologies, the demand for enhanced device computing capabilities has significantly increased. Neuromorphic computing, an emerging computational paradigm inspired by the human brain, has garnered growing attention as a promising research frontier. Inspired by the human brain’s functionality, this technology mimics the behavior of neurons and synapses to enable efficient, low-power computing. Unlike conventional digital systems, this approach offers a potentially superior alternative. This article delves into the appli

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Hao, Ji, Young-Hoon Kim, Severin N. Habisreutinger, et al. "Low-energy room-temperature optical switching in mixed-dimensionality nanoscale perovskite heterojunctions." Science Advances 7, no. 18 (2021): eabf1959. http://dx.doi.org/10.1126/sciadv.abf1959.

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Long-lived photon-stimulated conductance changes in solid-state materials can enable optical memory and brain-inspired neuromorphic information processing. It remains challenging to realize optical switching with low-energy consumption, and new mechanisms and design principles giving rise to persistent photoconductivity (PPC) can help overcome an important technological hurdle. Here, we demonstrate versatile heterojunctions between metal-halide perovskite nanocrystals and semiconducting single-walled carbon nanotubes that enable room-temperature, long-lived (thousands of seconds), writable, an

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Plummer, Douglas Z., Emily D’Alessandro, Aidan Burrowes, Joshua Fleischer, Alexander M. Heard, and Yingying Wu. "2D Spintronics for Neuromorphic Computing with Scalability and Energy Efficiency." Journal of Low Power Electronics and Applications 15, no. 2 (2025): 16. https://doi.org/10.3390/jlpea15020016.

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The demand for computing power has been growing exponentially with the rise of artificial intelligence (AI), machine learning, and the Internet of Things (IoT). This growth requires unconventional computing primitives that prioritize energy efficiency, while also addressing the critical need for scalability. Neuromorphic computing, inspired by the biological brain, offers a transformative paradigm for addressing these challenges. This review paper provides an overview of advancements in 2D spintronics and device architectures designed for neuromorphic applications, with a focus on techniques s

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Kim, So-Yeon. "Operating Mechanism Principles and Advancements for Halide Perovskite-Based Memristors and Neuromorphic Devices." Journal of Physical Chemistry Letters 15 (September 30, 2024): 10087–103. https://doi.org/10.1021/acs.jpclett.4c02170.

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With the advent of the generation of artificial intelligence (AI) based on big data-processing technologies, next-generation memristor and memristive neuromorphic devices have been actively studied with great interest to overcome the von Neumann bottleneck limits. Among various candidates, halide perovskites (HPs) have been in the spotlight as potential candidates for these devices due to their unique switching characteristics with low energy consumption and flexible integration compatibility across various sources for scalability. We outline the characteristics and operating principles of HP-

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Hajtó, Dániel, Ádám Rák, and György Cserey. "Robust Memristor Networks for Neuromorphic Computation Applications." Materials 12, no. 21 (2019): 3573. http://dx.doi.org/10.3390/ma12213573.

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One of the main obstacles for memristors to become commonly used in electrical engineering and in the field of artificial intelligence is the unreliability of physical implementations. A non-uniform range of resistance, low mass-production yield and high fault probability during operation are disadvantages of the current memristor technologies. In this article, the authors offer a solution for these problems with a circuit design, which consists of many memristors with a high operational variance that can form a more robust single memristor. The proposition is confirmed by physical device meas

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Moradi, Saber, and Rajit Manohar. "The impact of on-chip communication on memory technologies for neuromorphic systems." Journal of Physics D: Applied Physics 52, no. 1 (2018): 014003. http://dx.doi.org/10.1088/1361-6463/aae641.

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Bihari, Avadha. "Machine Learning-Driven Reconfigurable EONs with Neuromorphic Computing for Network Slicing and On-Demand Service Provisioning: A Review and Survey." International Journal for Research in Applied Science and Engineering Technology 12, no. 8 (2024): 336–43. http://dx.doi.org/10.22214/ijraset.2024.63890.

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Abstract: This paper delves into the substantial potential of integrating advanced technologies within Reconfigurable Elastic Optical Networks (REONs). By leveraging machine learning and neuromorphic computing, these networks can significantly enhance performance, scalability, and efficiency. Machine learning models facilitate dynamic resource management, allowing for the on-demand reconfiguration of optical networks to improve service provisioning and maintain high Quality of Service (QoS). Neuromorphic processors further boost network-slicing capabilities, optimizing bandwidth management and

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Rusev, Georgi, Svetlozar Yordanov, Simona Nedelcheva, et al. "Decoding Brain Signals in a Neuromorphic Framework for a Personalized Adaptive Control of Human Prosthetics." Biomimetics 10, no. 3 (2025): 183. https://doi.org/10.3390/biomimetics10030183.

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Current technological solutions for Brain-machine Interfaces (BMI) achieve reasonable accuracy, but most systems are large in size, power consuming and not auto-adaptive. This work addresses the question whether current neuromorphic technologies could resolve these problems? The paper proposes a novel neuromorphic framework of a BMI system for prosthetics control via decoding Electro Cortico-Graphic (ECoG) brain signals. It includes a three-dimensional spike timing neural network (3D-SNN) for brain signals features extraction and an on-line trainable recurrent reservoir structure (Echo state n

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Huhs, Niklas, Niloofar Kalashtari, Jens Kraitl, Christoph Hornberger, and Olaf Simanski. "Non-invasive vital parameter detection using neuromorphic cameras." Current Directions in Biomedical Engineering 10, no. 4 (2024): 332–35. https://doi.org/10.1515/cdbme-2024-2081.

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Abstract The non-invasive detection of vital parameters is gaining significance in the medical field through the use of various established and novel camera technologies and the use of artificial intelligence for data processing. A recently emerging type of imaging sensor, the neuromorphic camera, mimics the way the human eye perceives changes in its vision by responding to local changes in brightness for each pixel. The use of these neuromorphic cameras in the medical field is mostly unexplored and offers new possibilities. This research experiment is designed as a proof of concept for the us

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Wan, Changjin, Mengjiao Pei, Kailu Shi, et al. "Toward a Brain‐Neuromorphics Interface." Advanced Materials, February 10, 2024. http://dx.doi.org/10.1002/adma.202311288.

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AbstractBrain‐computer interfaces (BCIs) that enable human‐machine interaction have immense potential in restoring or augmenting human capabilities. Traditional BCIs are realized based on complementary metal‐oxide‐semiconductor (CMOS) technologies with complex, bulky, and low biocompatible circuits, and suffer with the low energy efficiency of the von Neumann architecture. The brain‐neuromorphics interface (BNI) would offer a promising solution to advance the BCI technologies and shape our interactions with machineries. Neuromorphic devices and systems are able to provide substantial computati

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"Vision Technologies for Smartphones." New Electronics 56, no. 3 (2023): 31. http://dx.doi.org/10.12968/s0047-9624(23)60547-4.

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Bartolozzi, Chiara, Giacomo Indiveri, and Elisa Donati. "Embodied neuromorphic intelligence." Nature Communications 13, no. 1 (2022). http://dx.doi.org/10.1038/s41467-022-28487-2.

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AbstractThe design of robots that interact autonomously with the environment and exhibit complex behaviours is an open challenge that can benefit from understanding what makes living beings fit to act in the world. Neuromorphic engineering studies neural computational principles to develop technologies that can provide a computing substrate for building compact and low-power processing systems. We discuss why endowing robots with neuromorphic technologies – from perception to motor control – represents a promising approach for the creation of robots which can seamlessly integrate in society. W

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Yan, Jiongyi, Yutai Su, James P. K. Armstrong, and Andrew Gleadall. "Additive Manufacturing of Neuromorphic Systems." Advanced Materials, July 14, 2025. https://doi.org/10.1002/adma.202504807.

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AbstractNeuromorphic engineering aims to create brain‐inspired computing systems based on synaptic electronic hardware and neural network software. It combines intelligent materials, advanced processing technology, and computation programs. Additive manufacturing (AM), despite being one of the advanced manufacturing technologies capable of multimaterial processing at the microscale, is not widely applied in neuromorphic hardware fabrication. This gap suggests not only process incompatibility and limited resolution of AM but opportunities to create novel intelligent systems. Here, the state‐of‐

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Cramer, Benjamin, Sebastian Billaudelle, Simeon Kanya, et al. "Surrogate gradients for analog neuromorphic computing." Proceedings of the National Academy of Sciences 119, no. 4 (2022). http://dx.doi.org/10.1073/pnas.2109194119.

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Significance Neuromorphic systems aim to accomplish efficient computation in electronics by mirroring neurobiological principles. Taking advantage of neuromorphic technologies requires effective learning algorithms capable of instantiating high-performing neural networks, while also dealing with inevitable manufacturing variations of individual components, such as memristors or analog neurons. We present a learning framework resulting in bioinspired spiking neural networks with high performance, low inference latency, and sparse spike-coding schemes, which also self-corrects for device mismatc

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Mehonic, Adnan, Daniele Ielmini, Kaushik Roy, et al. "Roadmap to neuromorphic computing with emerging technologies." APL Materials 12, no. 10 (2024). http://dx.doi.org/10.1063/5.0179424.

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Moss, David. "Photonic Multiplexing Technologies for Optical Neuromorphic Networks." SSRN Electronic Journal, 2022. http://dx.doi.org/10.2139/ssrn.4204530.

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Decastri, Davide, and Francesca Borghi. "Advances in Neuromorphic Computing Devices: Insights on Both Conventional and Unconventional Architectures." Recent Patents on Nanotechnology 19 (February 10, 2025). https://doi.org/10.2174/0118722105335459241210043513.

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Abstract: Neuromorphic circuits and devices have been introduced in the last decades as elements of a key strategy for developing of new paradigms of computation, inspired by the intent to mimic elementary neuron structure and biological mechanisms, for the overcoming of energy and timeconsuming bottlenecks achieved by digital computing (DC) technologies. Although the term “neuromorphic” is in common use, its meaning is often misunderstood and indistinctly associated with many different technologies, based on both conventional and unconventional electronic components and architectures. Here an

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