Relevant bibliographies by topics / Neuromorphic applications

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Neuromorphic applications'

Author: Grafiati
Published: 6 September 2023
Last updated: 26 July 2025

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Journal articles on the topic "Neuromorphic applications"

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Bi, Jinming, Yanran Li, Rong Lu, Honglin Song, and Jie Jiang. "Electrolyte-gated optoelectronic transistors for neuromorphic applications." Journal of Semiconductors 46, no. 2 (2025): 021401. https://doi.org/10.1088/1674-4926/24090042.

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Abstract The traditional von Neumann architecture has demonstrated inefficiencies in parallel computing and adaptive learning, rendering it incapable of meeting the growing demand for efficient and high-speed computing. Neuromorphic computing with significant advantages such as high parallelism and ultra-low power consumption is regarded as a promising pathway to overcome the limitations of conventional computers and achieve the next-generation artificial intelligence. Among various neuromorphic devices, the artificial synapses based on electrolyte-gated transistors stand out due to their low
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Park, Jisoo, Jihyun Shin, and Hocheon Yoo. "Heterostructure-Based Optoelectronic Neuromorphic Devices." Electronics 13, no. 6 (2024): 1076. http://dx.doi.org/10.3390/electronics13061076.

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The concept of neuromorphic devices, aiming to process large amounts of information in parallel, at low power, high speed, and high efficiency, is to mimic the functions of human brain by emulating biological neural behavior. Optoelectronic neuromorphic devices are particularly suitable for neuromorphic applications with their ability to generate various pulses based on wavelength and to control synaptic stimulation. Each wavelength (ultraviolet, visible, and infrared) has specific advantages and optimal applications. Here, the heterostructure-based optoelectronic neuromorphic devices are expl
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Schuman, Catherine. "(Invited) Application-Hardware Co-Design for Neuromorphic Computing Systems." ECS Meeting Abstracts MA2025-01, no. 63 (2025): 3082. https://doi.org/10.1149/ma2025-01633082mtgabs.

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Neuromorphic computing offers the opportunity for low-power, intelligent autonomous systems. However, effectively leveraging neuromorphic computers requires co-design of hardware, algorithms, and applications. In this talk, I will review our recent work on hardware-application co-design in neuromorphic computing. I will discuss co-design results with a variety of neuromorphic devices, including memristors and ferroelectric devices. I will also present several applications of neuromorphic computing, including autonomous vehicles and internal combustion engine control.
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Mikki, Said. "Generalized Neuromorphism and Artificial Intelligence: Dynamics in Memory Space." Symmetry 16, no. 4 (2024): 492. http://dx.doi.org/10.3390/sym16040492.

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This paper introduces a multidisciplinary conceptual perspective encompassing artificial intelligence (AI), artificial general intelligence (AGI), and cybernetics, framed within what we call the formalism of generalized neuromorphism. Drawing from recent advancements in computing, such as neuromorphic computing and spiking neural networks, as well as principles from the theory of open dynamical systems and stochastic classical and quantum dynamics, this formalism is tailored to model generic networks comprising abstract processing events. A pivotal aspect of our approach is the incorporation o
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Henkel, Jorg. "Stochastic Computing for Neuromorphic Applications." IEEE Design & Test 38, no. 6 (2021): 4. http://dx.doi.org/10.1109/mdat.2021.3126288.

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Wang, Weisheng, and Liqiang Zhu. "Electrolyte Gated Transistors for Brain Inspired Neuromorphic Computing and Perception Applications: A Review." Nanomaterials 15, no. 5 (2025): 348. https://doi.org/10.3390/nano15050348.

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Emerging neuromorphic computing offers a promising and energy-efficient approach to developing advanced intelligent systems by mimicking the information processing modes of the human brain. Moreover, inspired by the high parallelism, fault tolerance, adaptability, and low power consumption of brain perceptual systems, replicating these efficient and intelligent systems at a hardware level will endow artificial intelligence (AI) and neuromorphic engineering with unparalleled appeal. Therefore, construction of neuromorphic devices that can simulate neural and synaptic behaviors are crucial for a
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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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Schuman, Catherine, Robert Patton, Shruti Kulkarni, et al. "Evolutionary vs imitation learning for neuromorphic control at the edge*." Neuromorphic Computing and Engineering 2, no. 1 (2022): 014002. http://dx.doi.org/10.1088/2634-4386/ac45e7.

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Abstract Neuromorphic computing offers the opportunity to implement extremely low power artificial intelligence at the edge. Control applications, such as autonomous vehicles and robotics, are also of great interest for neuromorphic systems at the edge. It is not clear, however, what the best neuromorphic training approaches are for control applications at the edge. In this work, we implement and compare the performance of evolutionary optimization and imitation learning approaches on an autonomous race car control task using an edge neuromorphic implementation. We show that the evolutionary a
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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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Dissertations / Theses on the topic "Neuromorphic applications"

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Chen, Xing. "Modeling and simulations of skyrmionic neuromorphic applications." Thesis, université Paris-Saclay, 2022. http://www.theses.fr/2022UPAST083.

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Les nanodispositifs spintroniques, qui exploitent à la fois les propriétés magnétiques et électriques des électrons, apportent diverses caractéristiques intéressantes et prometteuses pour le calcul neuromorphique. Les textures magnétiques, telles que les parois de domaine et les skyrmions, sont particulièrement intrigantes en tant que composants neuromorphiques, car elles peuvent prendre en charge différentes fonctionnalités grâce à la richesse de leurs mécanismes physiques. La façon dont la dynamique des skyrmions peut être utilisée pour construire du matériel neuromorphique économe en énergi
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Shi, Yuanyuan. "Two dimensional materials based electronic synapses for neuromorphic applications." Doctoral thesis, Universitat de Barcelona, 2018. http://hdl.handle.net/10803/663415.

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Electronic machines and computers have experienced a huge development during the last four decades, mainly thanks to the continuous scaling down of the hardware responsible of information processing and storage (i.e. transistors). However, as the size of these devices approaches inter-atomic distances, the fabrication costs increase exponentially. In order to solve this problem, the industry has started to consider new system architectures and hardware for processing and storing information. Inspired by nature, scientists and engineers have focused their attention on the human brain, which is
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Uppala, Roshni. "Simulating Large Scale Memristor Based Crossbar for Neuromorphic Applications." University of Dayton / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1429296073.

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Lai, Qianxi. "Electrically configurable materials and devices for intelligent neuromorphic applications." Diss., Restricted to subscribing institutions, 2009. http://proquest.umi.com/pqdweb?did=1872061101&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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Mandal, Saptarshi. "Study of Mn doped HfO2 based Synaptic Devices for Neuromorphic Applications." University of Toledo / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1384535471.

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Jouni, Zalfa. "Analog spike-based neuromorphic computing for low-power smart IoT applications." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPAST114.

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Avec l'expansion de l'Internet des objets (IoT) et l'augmentation des appareils connectés et des communications complexes, la demande de technologies de localisation précises et économes en énergie s'est intensifiée. Les techniques traditionnelles de machine learning et d'intelligence artificielle (IA) offrent une haute précision dans la localisation par radiofréquence (RF), mais au prix d'une complexité accrue et d'une consommation d'énergie élevée. Pour relever ces défis, cette thèse explore le potentiel de l'informatique neuromorphique, inspirée par les mécanismes du cerveau, pour permettre
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Pedró, Puig Marta. "Implementation of unsupervised learning mechanisms on OxRAM devices for neuromorphic computing applications." Doctoral thesis, Universitat Autònoma de Barcelona, 2019. http://hdl.handle.net/10803/667894.

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La present tesi recull els resultats de la recerca orientada a aportar una metodologia de caracterització elèctrica, modelat i simulació per a dispositius de commutació resistiva, quan es consideren aplicacions de computació neuromòrfica basades en aprenentatge no-supervisat, àmpliament demandades en l’actualitat com a solució de baix consum a les següents problemàtiques: per una banda, la limitació de la velocitat en la transferència de dades entre les unitats de memoria i processament que té lloc en les arquitectures de computador convencional (von Neumann). Per altra banda, la necessitat cr
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Petre, Csaba. "Sim2spice a tool for compiling simulink designs on FPAA and applications to neuromorphic circuits /." Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31820.

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Thesis (M. S.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2010.<br>Committee Chair: Paul Hasler; Committee Member: Christopher Rozell; Committee Member: David Anderson. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Herrmann, Eric. "A Novel Gate Controlled Metal Oxide Resistive Memory Cell and its Applications." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1540565326482153.

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MARRONE, FRANCESCO. "Memristor-based hardware accelerators: from device modeling to AI applications." Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2972305.

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Books on the topic "Neuromorphic applications"

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Kozma, Robert, Robinson E. Pino, and Giovanni E. Pazienza, eds. Advances in Neuromorphic Memristor Science and Applications. Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-4491-2.

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Kozma, Robert. Advances in Neuromorphic Memristor Science and Applications. Springer Netherlands, 2012.

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Beaton, Paul Timothy, ed. Frontiers in Memristive Materials for Neuromorphic Processing Applications. National Academies Press, 2020. http://dx.doi.org/10.17226/25938.

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C, Merrill Walter, and United States. National Aeronautics and Space Administration. Scientific and Technical Information Division., eds. Neuromorphic learning of continuous-valued mappings from noise-corrupted data: Application to real-time adaptive control. National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1990.

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Bartolozzi, Chiara, Emre O. Neftci, and Elisabetta Chicca, eds. Neuromorphic Engineering Systems and Applications. Frontiers Media SA, 2021. http://dx.doi.org/10.3389/978-2-88971-723-1.

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van Schaik, André, Tobi Delbruck, and Jennifer Hasler, eds. Neuromorphic Engineering Systems and Applications. Frontiers Media SA, 2015. http://dx.doi.org/10.3389/978-2-88919-454-4.

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Dong, Yibo, Min Gu, Elena Goi, Yangyundou Wang, and Zhengfen Wan. Neuromorphic Photonic Devices and Applications. Elsevier, 2023.

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Wang, Jing, Min Gu, Elena Goi, Yangyundou Wang, and Zhengfen Wan. Neuromorphic Photonic Devices and Applications. Elsevier, 2023.

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Pazienza, Giovanni E., Robert Kozma, and Robinson E. Pino. Advances in Neuromorphic Memristor Science and Applications. Springer Netherlands, 2016.

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Advances In Neuromorphic Memristor Science And Applications. Springer, 2012.

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Book chapters on the topic "Neuromorphic applications"

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Firoozi, Ali Akbar, and Ali Asghar Firoozi. "Case Studies and Real-World Applications." In Neuromorphic Computing. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-65549-4_8.

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Narduzzi, Simon, Loreto Mateu, Petar Jokic, Erfan Azarkhish, and Andrea Dunbar. "Benchmarking Neuromorphic Computing for Inference." In Industrial Artificial Intelligence Technologies and Applications. River Publishers, 2023. http://dx.doi.org/10.1201/9781003377382-1.

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Milo, Valerio, Gerardo Malavena, Christian Monzio Compagnoni, and Daniele Ielmini. "Memristive/CMOS Devices for Neuromorphic Applications." In Springer Handbook of Semiconductor Devices. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-79827-7_32.

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Lu, Wei. "RRAM Fabric for Neuromorphic Computing Applications." In From Artificial Intelligence to Brain Intelligence. River Publishers, 2022. http://dx.doi.org/10.1201/9781003338215-10.

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Bhowmik, Debanjan. "Spintronic Oscillators, Their Synchronization Properties, and Applications in Oscillatory Neural Networks (ONNs)." In Spintronics-Based Neuromorphic Computing. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-4445-9_7.

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Gómez-Vilda, Pedro, José Manuel Ferrández-Vicente, Victoria Rodellar-Biarge, et al. "Neuromorphic Detection of Vowel Representation Spaces." In New Challenges on Bioinspired Applications. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21326-7_1.

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Hu, Xiaofang, Shukai Duan, Wenbo Song, Jiagui Wu, and Pinaki Mazumder. "Memristor-based Cellular Nonlinear/Neural Network: Design, Analysis and Applications." In Neuromorphic Circuits for Nanoscale Devices. River Publishers, 2022. http://dx.doi.org/10.1201/9781003338918-11.

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Isik, Murat, Hiruna Vishwamith, Yusuf Sur, Kayode Inadagbo, and I. Can Dikmen. "NEUROSEC: FPGA-Based Neuromorphic Audio Security." In Applied Reconfigurable Computing. Architectures, Tools, and Applications. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-55673-9_10.

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Pino, Robinson E. "Computational Intelligence and Neuromorphic Computing Architectures." In Advances in Neuromorphic Memristor Science and Applications. Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-4491-2_6.

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Ryan, Kevin, Sansiri Tanachutiwat, and Wei Wang. "3D CMOL Crossnet for Neuromorphic Network Applications." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02427-6_1.

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Conference papers on the topic "Neuromorphic applications"

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Shoesmith, Thomas, James C. Knight, Balazs Meszaros, Jonathan Timcheck, and Thomas Nowotny. "Eventprop training for efficient neuromorphic applications." In 2025 Neuro Inspired Computational Elements (NICE). IEEE, 2025. https://doi.org/10.1109/nice65350.2025.11064940.

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Gobin, Derek, Shay Snyder, Guojing Cong, Shruti R. Kulkarni, Catherine Schuman, and Maryam Parsa. "Exploration of Novel Neuromorphic Methodologies for Materials Applications." In 2024 International Conference on Neuromorphic Systems (ICONS). IEEE, 2024. https://doi.org/10.1109/icons62911.2024.00049.

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Dias, Lília M. S., Lianshe Fu, Elias Towe, Rute A. S. Ferreira, and Paulo S. B. André. "Luminescent Waveguides with Synaptic Properties for Photonic Artificial Neural Networks." In CLEO: Applications and Technology. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cleo_at.2024.jtu2a.12.

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We replicate biological neurons and synapses, transmitting 0.2 Hz impulses through luminescent waveguides with adjustable features. This breakthrough has significant implications for neuromorphic engineering, providing valuable insights into neural networks technological applications and signal transmission.
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Schmitt, Erika, Sanchit Gupta, and Patrick Abbs. "Continuous Learning for Real-Time Auditory Blind Source Separation Applications." In 2024 International Conference on Neuromorphic Systems (ICONS). IEEE, 2024. https://doi.org/10.1109/icons62911.2024.00021.

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Yang, Hua, Lihao Yu, Yanjie Lv, et al. "Multi-Strategy Collaborative Improved Seagull Optimization Algorithm and Its Applications." In 2024 International Conference on Neuromorphic Computing (ICNC). IEEE, 2024. https://doi.org/10.1109/icnc64304.2024.10987897.

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Maier, Patrick, James Rainey, Elena Gheorghiu, Kofi Appiah, and Deepayan Bhowmik. "Digit classification using biologically plausible neuromorphic vision." In Applications of Digital Image Processing XLVII, edited by Andrew G. Tescher and Touradj Ebrahimi. SPIE, 2024. http://dx.doi.org/10.1117/12.3031280.

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Panes-Ruiz, Luis Antonio, Shirong Huang, Alon Ascoli, Ronald Tetzlaff, and Gianaurelio Cuniberti. "Carbon Nanomaterial-Based Memristive Devices for Neuromorphic Applications." In 2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE). IEEE, 2024. https://doi.org/10.1109/metroxraine62247.2024.10796782.

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Shainline, Jeffrey M. "Superconducting optoelectronic networks for neuromorphic supercomputing." In Photonic Computing: From Materials and Devices to Systems and Applications, edited by Xingjie Ni and Wenshan Cai. SPIE, 2024. http://dx.doi.org/10.1117/12.3029900.

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Ali, Teymoor, James Rainey, Sook Yen Lau, et al. "An FPGA-based neuromorphic vision system accelerator." In Artificial Intelligence for Security and Defence Applications II, edited by Henri Bouma, Yitzhak Yitzhaky, Radhakrishna Prabhu, and Hugo J. Kuijf. SPIE, 2024. http://dx.doi.org/10.1117/12.3034095.

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Chen, Rongzhou, Shuo Zhu, Chutian Wang, and Edmund Y. Lam. "Spectrum synthesis with computational neuromorphic imaging." In Computational Optical Sensing and Imaging. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cosi.2024.cm2b.3.

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We propose a method for spectrum synthesis via computational neuromorphic imaging (CNI), employing stochastic variational inference to extract spectral profiles from dynamic light-sample interactions. It provides new insights into biological analysis and CNI applications.
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Reports on the topic "Neuromorphic applications"

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Pasupuleti, Murali Krishna. Neuromorphic Nanotech: 2D Materials for Energy-Efficient Edge Computing. National Education Services, 2025. https://doi.org/10.62311/nesx/rr325.

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Abstract The demand for energy-efficient, real-time computing is driving the evolution of neuromorphic computing and edge AI systems. Traditional silicon-based processors struggle with power inefficiencies, memory bottlenecks, and scalability limitations, making them unsuitable for next-generation low-power AI applications. This research report explores how 2D materials, such as graphene, transition metal dichalcogenides (TMDs), black phosphorus, and MXenes, are enabling the development of neuromorphic architectures that mimic biological neural networks for high-speed, ultra-low-power computat
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Davis, Joel L. Neuromorphic Systems: From Biological Foundations to System Properties and Real World Applications. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada333498.

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Sankaranarayanan, Subramanian, and Aldo Romero. Applications of Nickelate perovskites for neuromorphic computing from electronic structure and Machine Learning. Office of Scientific and Technical Information (OSTI), 2025. https://doi.org/10.2172/2531093.

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Pasupuleti, Murali Krishna. Neural Computation and Learning Theory: Expressivity, Dynamics, and Biologically Inspired AI. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv425.

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Abstract: Neural computation and learning theory provide the foundational principles for understanding how artificial and biological neural networks encode, process, and learn from data. This research explores expressivity, computational dynamics, and biologically inspired AI, focusing on theoretical expressivity limits, infinite-width neural networks, recurrent and spiking neural networks, attractor models, and synaptic plasticity. The study investigates mathematical models of function approximation, kernel methods, dynamical systems, and stability properties to assess the generalization capa
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Potok, Thomas, Catherine Schuman, Robert Patton, Todd Hylton, Hai Li, and Robinson Pino. Neuromorphic Computing, Architectures, Models, and Applications. A Beyond-CMOS Approach to Future Computing, June 29-July 1, 2016, Oak Ridge, TN. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1341738.

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Pasupuleti, Murali Krishna. Next-Generation Extended Reality (XR): A Unified Framework for Integrating AR, VR, and AI-driven Immersive Technologies. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv325.

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Abstract: Extended Reality (XR), encompassing Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), is evolving into a transformative technology with applications in healthcare, education, industrial training, smart cities, and entertainment. This research presents a unified framework integrating AI-driven XR technologies with computer vision, deep learning, cloud computing, and 5G connectivity to enhance immersion, interactivity, and scalability. AI-powered neural rendering, real-time physics simulation, spatial computing, and gesture recognition enable more realistic and adap
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