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• Quadratic integrate-and-fire neurons exhibit less fragmented loss landscapes and outperform leaky integrate-and-fire neurons in spike-based gradient descent
  The ability to train spiking neural networks is essential for modeling biological neural networks as well as for neuromorphic computing. However, for the extensively used leaky integrate-and-fire (LIF) neurons, arbitrarily small parameter changes can induce sp…
  datacite Wenig, Carlo, Memmesheimer, Raoul-Martin, Klos, Christian 2026 置信度 0.66 Neural and Evolutionary Computing (cs.NE)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciencesI.2.6
• Second-Order Synaptic Memory using Inherent Plasticity of Moiré Superlattices
  Achieving synaptic functionality electronically in a single-element quantum material is a fundamental challenge, as conventional methods rely on the introduction of extrinsic charge-traps or polar components. Here, we demonstrate that twisted double bilayer gr…
  datacite Ahmed, Tanweer, Watanabe, Kenji, Taniguchi, Takashi, Casanova, Fèlix 等 2026 置信度 0.66 Mesoscale and Nanoscale Physics (cond-mat.mes-hall)Materials Science (cond-mat.mtrl-sci)FOS: Physical sciencesFOS: Physical sciences
• BIT-X ∞ / SE-08: Stress-Adaptive Analog Gating Circuit for Edge Sensors
  SE-08 ra đời sau khi SE-07 hoàn thành benchmark phần mềm. Câu hỏi tự nhiên tiếp theo: nếu logic lọc tải Ω(t) đã chứng minh được trong sandbox — liệu nó có thể được thực thi bằng phần cứng analog, không cần CPU? SE-08 đề xuất kiến trúc mạch ba khối (Analog Abso…
  datacite Trịnh, Bùi Quang 2026 置信度 0.66 neuromorphicanalog circuitedge computingIoTboundary filter
• BIT-X ∞ / SE-08: Stress-Adaptive Analog Gating Circuit for Edge Sensors
  SE-08 ra đời sau khi SE-07 hoàn thành benchmark phần mềm. Câu hỏi tự nhiên tiếp theo: nếu logic lọc tải Ω(t) đã chứng minh được trong sandbox — liệu nó có thể được thực thi bằng phần cứng analog, không cần CPU? SE-08 đề xuất kiến trúc mạch ba khối (Analog Abso…
  datacite Trịnh, Bùi Quang 2026 置信度 0.66 neuromorphicanalog circuitedge computingIoTboundary filter
• BIT-X ∞ / SE-08: Stress-Adaptive Analog Gating Circuit for Edge Sensors
  SE-08 ra đời sau khi SE-07 hoàn thành benchmark phần mềm. Câu hỏi tự nhiên tiếp theo: nếu logic lọc tải Ω(t) đã chứng minh được trong sandbox — liệu nó có thể được thực thi bằng phần cứng analog, không cần CPU? SE-08 đề xuất kiến trúc mạch ba khối (Analog Abso…
  datacite Trịnh, Bùi Quang 2026 置信度 0.66 neuromorphicanalog circuitedge computingIoTboundary filter
• Event2Vec: Processing Neuromorphic Events Directly by Representations in Vector Space
  Neuromorphic event cameras possess superior temporal resolution, power efficiency, and dynamic range compared to traditional cameras. However, their asynchronous and sparse data format poses a significant challenge for conventional deep learning methods. Most …
  datacite Fang, Wei, Panda, Priyadarshini 2025 置信度 0.66 Computer Vision and Pattern Recognition (cs.CV)Neural and Evolutionary Computing (cs.NE)FOS: Computer and information sciencesFOS: Computer and information sciences
• A 32-Channel 3.53-μW Per Channel Brain-Machine Interface SoC Featuring Dual-Threshold Delta-modulation, In-Memory Spike Detection and Bi-SNN Based Motor Decoding
  With the scaling of sensor channel counts, systems confront challenges in frontend data sensing and on-implant data processing. This work presents a 32-channel fully event-based iBMI SoC in 65nm CMOS for an efficient neuromorphic signal processing pipeline. Th…
  datacite Ke, Ye, Fu, Zhengnan, Sun, Pao-Sheng Vincent, Guo, An 等 2026 置信度 0.66 Signal Processing (eess.SP)FOS: Electrical engineering, electronic engineering, information engineeringFOS: Electrical engineering, electronic engineering, information engineering
• Emerging Non-Volatile Opto-electronic Resistive Memories for Next-Generation Photonic Integrated Circuits
  Photonic integrated circuits have emerged as a powerful platform for high speed communication, sensing, and information processing due to their large bandwidth, low latency, and inherent parallelism. However, the absence of efficient, scalable, and non-volatil…
  datacite Kumar, Santosh, Kumar, Mukesh, Shin, Eunso, Tossoun, Bassem 等 2026 置信度 0.66 Optics (physics.optics)FOS: Physical sciencesFOS: Physical sciences
• IO Pad Integrity in Energy-Efficient Neuromorphic Chips
  Neuromorphic computing relies on low-power, high-reliability hardware, yet the integrity of input/output pads (IOPADs) remains an underexplored factor affecting system performance. This chapter examines the role of IOPAD integrity in neuromorphic VLSI design a…
  datacite Ghani, Arfan 2026 置信度 0.66 Signal Processing (eess.SP)FOS: Electrical engineering, electronic engineering, information engineeringFOS: Electrical engineering, electronic engineering, information engineering
• Spiking and Event-driven Neuromorphic Mamba Models for Efficient Speech Recognition
  Deep learning has greatly advanced automatic speech recognition (ASR), enabling widespread deployment on edge devices such as smartphones and smart home systems. However, the computational and energy demands of deep neural networks pose significant challenges …
  datacite Ahmed, Tauseef, Sun, Tao, Castrillon, Jeronimo, Vadivel, Kanishkan 等 2026 置信度 0.66 Neural and Evolutionary Computing (cs.NE)Sound (cs.SD)FOS: Computer and information sciencesFOS: Computer and information sciences
• Wave-based Neuromorphic Circuit Networks: Tunable 2D Transmission-Line Metamaterials
  Neuromorphic computing promises fast and energy-efficient information processing for emerging applications such as artificial intelligence. This paper presents neuromorphic processors based on wave-based programmable transmission-line (TLIN) metamaterials. Spe…
  datacite Thakkar, Shrey, Grbic, Anthony 2026 置信度 0.66 Applied Physics (physics.app-ph)FOS: Physical sciencesFOS: Physical sciences
• SC-NeuroCore: Universal Stochastic Computing Framework for Neuromorphic Hardware
  A high-performance neuromorphic computing framework combining stochastic computing with spiking neural networks. 122 neuron models (1943-2026), Rust SIMD engine (111 models, 41.3 Gbit/s AVX-512, PyO3 bindings), bit-true Verilog RTL co-simulation, FPGA synthesi…
  datacite Sotek, Miroslav 2026 置信度 0.66 neuromorphicstochastic computingspiking neural networksFPGAhyper-dimensional computing
• SC-NeuroCore: Universal Stochastic Computing Framework for Neuromorphic Hardware
  A high-performance neuromorphic computing framework combining stochastic computing with spiking neural networks. 122 neuron models (1943-2026), Rust SIMD engine (111 models, 41.3 Gbit/s AVX-512, PyO3 bindings), bit-true Verilog RTL co-simulation, FPGA synthesi…
  datacite Sotek, Miroslav 2026 置信度 0.66 neuromorphicstochastic computingspiking neural networksFPGAhyper-dimensional computing
• Beyond Biological Replication: E8 Root Vector Lattice as a Superior Neuromorphic Substrate
  This paper presents E8 Lie group root vector lattice mathematics as a superior substrate for neuromorphic computing, in direct response to recent 3D neuron-electronic device research (Nature Electronics, April 2026). The author demonstrates that E8's 240 root …
  datacite Caldin, Andrew Stewart 2026 置信度 0.66 neuromorphic computingE8 Lie grouproot vector latticeartificial intelligenceconsciousness
• Beyond Biological Replication: E8 Root Vector Lattice as a Superior Neuromorphic Substrate
  This paper presents E8 Lie group root vector lattice mathematics as a superior substrate for neuromorphic computing, in direct response to recent 3D neuron-electronic device research (Nature Electronics, April 2026). The author demonstrates that E8's 240 root …
  datacite Caldin, Andrew Stewart 2026 置信度 0.66 neuromorphic computingE8 Lie grouproot vector latticeartificial intelligenceconsciousness
• Dynamic Latency Optimization for Edge-Based Machine Learning Models in 6G-Enabled Industrial Internet of Things (IIoT)
  Abstract The integration of 6G technology into the Industrial Internet of Things (IIoT) promises to redefine manufacturing through "Hyper-Reliable Low-Latency Communication" (HRLLC). However, the deployment of complex Machine Learning (ML) models at the edge r…
  datacite Seema Patil, Harshavardhana Doddamani, Savitha A C, Julianne Rivers 2026 置信度 0.66 6G Networks, Industrial IoT (IIoT), Edge Intelligence, Deep Reinforcement Learning, Latency Optimization
• Dynamic Latency Optimization for Edge-Based Machine Learning Models in 6G-Enabled Industrial Internet of Things (IIoT)
  Abstract The integration of 6G technology into the Industrial Internet of Things (IIoT) promises to redefine manufacturing through "Hyper-Reliable Low-Latency Communication" (HRLLC). However, the deployment of complex Machine Learning (ML) models at the edge r…
  datacite Seema Patil, Harshavardhana Doddamani, Savitha A C, Julianne Rivers 2026 置信度 0.66 6G Networks, Industrial IoT (IIoT), Edge Intelligence, Deep Reinforcement Learning, Latency Optimization
• ADAM-PentaD (QNexus): A Sovereign Complex-Spectral Wavefront Accelerator with Clifford-Algebra Compiler for Cognitively-Immune LLM Inference | [RESEARCH ***HYPOTHESIS***] | [wAI~wErrors, please focus on the "vector"=vision] | [NO HDL --- ONLY CONCEPT]
  Dear Colleagues, This is an open invitation to academic and independent research groups specializing in silicon photonics, materials science, hardware architecture, and geometric mathematics to investigate and validate a non-von Neumann computational hypothesi…
  datacite Kovnatsky, Artiom 2026 置信度 0.66 In-Memory ComputingMemristor CrossbarsPhotonic Unitary MeshesLow-Rank ApproximationRotary/Spherical Embeddings
• Compact and Energy-Efficient Memristive Spiking Neuromorphic Accelerator for Bio-inspired Interception Tasks
  Spiking neural networks (SNNs) provide an efficient event-driven computing paradigm for bio-inspired interception tasks. However, most implementations rely on von Neumann digital computing platforms, where memory and computation bottlenecks limit energy effici…
  datacite Qu, Qianhou, Lu, Sheng, Jung, Sungyong, Liang, Qilian 等 2026 置信度 0.66 Emerging Technologies (cs.ET)FOS: Computer and information sciencesFOS: Computer and information sciences
• Novel scanning probe methods for manipulating and characterising pentacene thin films and crystals
  Molecular semiconductors are rapidly emerging as a key enabling technology in fields such as quantum sensing, neuromorphic computing and energy generation. Their flexibility, tunability and low-cost fabrication make them promising materials for the highly-inte…
  datacite Bryan, Emma Grace 2025 置信度 0.66
• Symbiotic Dual-Brain Intelligence (SDBI) v1.0 - A Task-Routing Cognitive Architecture for Human–AI Co-Evolution
  Abstract Artificial intelligence is increasingly capable of executing structured cognitive tasks, yet the fundamental question remains: how should human and artificial intelligence be organized into a coherent cognitive system? This paper introduces Symbiotic …
  datacite Xu, Lucas Xiaochun 2026 置信度 0.66 Symbiotic Dual-Brain Intelligence (SDBI)Human-AI CollaborationCognitive ArchitectureTask RoutingDistributed Intelligence
• Symbiotic Dual-Brain Intelligence (SDBI) v1.0 - A Task-Routing Cognitive Architecture for Human–AI Co-Evolution
  Abstract Artificial intelligence is increasingly capable of executing structured cognitive tasks, yet the fundamental question remains: how should human and artificial intelligence be organized into a coherent cognitive system? This paper introduces Symbiotic …
  datacite Xu, Lucas Xiaochun 2026 置信度 0.66 Symbiotic Dual-Brain Intelligence (SDBI)Human-AI CollaborationCognitive ArchitectureTask RoutingDistributed Intelligence
• Understanding the H_φ Theory — A Guide for Non-Specialists: What the Theory Says, Why It Is Original, and What It Would Change If Verified
  Accessible introduction to the H_φ theory for readers without formal training in physics, mathematics or psychology. Presents the central hypothesis — that the Unus Mundus postulated by Jung and Pauli is a Hilbert space H_φ whose structure is entirely determin…
  datacite Alexandre Joseph Etienne Perez 2026 置信度 0.66 golden ratioUnus Mundusbrain organoidsBerry phasescience communication
• Sparse Photonic Reservoirs with Adaptive Cavity Memory and Polarized Noise: Surpassing LSTMs on Nonlinear Memory Tasks
  We present a sparse photonic reservoir computer with O(N)\mathcal{O}(N)O(N) small-world topology, adaptive optical cavity memory, and spatially correlated polarized noise that achieves NMSE = 0.050 on the NARMA10 benchmark, within 12.6% of dense Echo State Net…
  datacite Massami Okushigue, Jefferson 2026 置信度 0.66 photonic reservoir computing, neuromorphic photonics, silicon photonics, echo state networks, nonlinear time series prediction, optical cavities, polarized noise engineering, inverse design, sparse neural networks, NARMA benchmark
• Sparse Photonic Reservoirs with Adaptive Cavity Memory and Polarized Noise: Surpassing LSTMs on Nonlinear Memory Tasks
  We present a sparse photonic reservoir computer with O(N)\mathcal{O}(N)O(N) small-world topology, adaptive optical cavity memory, and spatially correlated polarized noise that achieves NMSE = 0.050 on the NARMA10 benchmark, within 12.6% of dense Echo State Net…
  datacite Massami Okushigue, Jefferson 2026 置信度 0.66 photonic reservoir computing, neuromorphic photonics, silicon photonics, echo state networks, nonlinear time series prediction, optical cavities, polarized noise engineering, inverse design, sparse neural networks, NARMA benchmark
• Scalable construction of spiking neural networks using up to thousands of GPUs
  Neuromorphic computing and engineering 6(2), 024012 (2026). doi:10.1088/2634-4386/ae65d2
  datacite Golosio, Bruno, Tiddia, Gianmarco, Villamar, José, Pontisso, Luca 等 2026 置信度 0.66 621.3
• Reconfigurable Multistate MRAM Synapses with Vortex STNO based Neurons for Scalable In-Memory Convolutional Neural Networks
  Magnetic tunnel junction (MTJ)-based magnetic random-access memory (MRAM) is a promising platform for neuromorphic and in-memory computing owing to its non-volatility, high endurance, fast switching dynamics and CMOS compatibility. However, conventional spin-t…
  datacite Raj, Ravish Kumar, Richter, Simon N., Ivriq, Saeed Baghaee, Fridorf, Oliver 等 2026 置信度 0.66 Applied Physics (physics.app-ph)Image and Video Processing (eess.IV)FOS: Physical sciencesFOS: Physical sciencesFOS: Electrical engineering, electronic engineering, information engineering
• Constant Depth Threshold Circuits For Exhaustive Epistasis Detection
  The development of large-scale neuromorphic hardware has made practical implementations of threshold gate-based circuits a near-term possibility. The complexity advantages regarding traditional computing classes, as evidenced in the literature, have prompted u…
  datacite Ribeiro, André, Ilic, Aleksandar, Sousa, Leonel 2026 置信度 0.66 Hardware Architecture (cs.AR)FOS: Computer and information sciencesFOS: Computer and information sciences
• Field-Driven Hybrid Filament Formation Governs Switching in Ta-HfO$_2$-Pt Memristors
  Memristive devices have gained significant attention for their potential in next-generation non-volatile memory and neuromorphic computing architectures. Among emerging candidates, transition metal oxides have proven particularly promising. While the switching…
  datacite Amaram, Ashutosh Krishna, Koneru, Aditya, Sankaranarayanan, Subramanian KRS 2026 置信度 0.66 Materials Science (cond-mat.mtrl-sci)Mesoscale and Nanoscale Physics (cond-mat.mes-hall)FOS: Physical sciencesFOS: Physical sciences
• Diagnostic Convergences of The Janus Machine: Independent Derivation and the Structure of Life, Mind, and Meaning
  The Janus Machine is a hardware-first synthetic organismic architecture for cognition, developed from first principles. This paper maps 33 diagnostic convergences between the architecture and established frameworks across 8 disciplines: philosophy, biology, cy…
  datacite Janus, Anthony 2026 置信度 0.66 EnactivismAutopoiesisBioelectric CognitionDissipative StructuresConatus
• Diagnostic Convergences of The Janus Machine: Independent Derivation and the Structure of Life, Mind, and Meaning
  The Janus Machine is a hardware-first synthetic organismic architecture for cognition, developed from first principles. This paper maps 33 diagnostic convergences between the architecture and established frameworks across 8 disciplines: philosophy, biology, cy…
  datacite Janus, Anthony 2026 置信度 0.66 EnactivismAutopoiesisBioelectric CognitionDissipative StructuresConatus
• Beyond silicon: materials, mechanisms, and methods for physical neural computing
  Physical implementations of neural computation now extend far beyond silicon hardware, encompassing substrates such as memristive devices, photonic circuits, mechanical metamaterials, microfluidic networks, chemical reaction systems, and living neural tissue. …
  datacite Fischer, Stefan, Ay, Nihat, Landsiedel, Olaf, Mohammadi, Esfandiar 等 2026 置信度 0.66 benchmarking frameworksin-memory computingmechanical metamaterialsmemristive systemsmicrofluidic and chemical computing
• Verdigraph NeuroGenesis: A Software Framework for Self-Evolving AI-Agent Cognitive Substrates
  Verdigraph NeuroGenesis v0.2.0 — software framework for self-evolving AI-agent cognitive substrates with mechanically verified operational invariants. Verdigraph is built on a simple observation: AI agents waste energy at industrial scale, and energy is carbon…
  datacite Hart, Justin 2026 置信度 0.66 artificial intelligenceAI agentsagent frameworksModel Context ProtocolMCP
• Verdigraph NeuroGenesis: A Software Framework for Self-Evolving AI-Agent Cognitive Substrates
  Verdigraph NeuroGenesis v0.1.0 — initial public release of an open-source framework for self-evolving AI-agent cognitive substrates. Defensive publication and citable software release. Verdigraph is built on a simple observation: AI agents waste energy at indu…
  datacite Hart, Justin 2026 置信度 0.66 artificial intelligenceAI agentsagent frameworksModel Context ProtocolMCP
• Advances in Organic In‐Sensor Neuromorphic Computing: from Material Mechanisms to Applications
  crossref Dong Hyun Lee, Woojo Kim, Eun Kwang Lee, Hocheon Yoo 2025-12-09T01:16:29Z 置信度 0.70
• Invited Talk: Neuromorphic Computing Principles, Achievements, and Potentials
  crossref Karlheinz Meier 2017-02-28T08:33:22Z 置信度 0.70
• Special Topic on Nonvolatile Memory for Efficient Implementation of Neural/Neuromorphic Computing
  crossref Shimeng Yu 2019-06-07T20:12:23Z 置信度 0.70
• Automatic Quality Evaluation for User Generated Contents in Online Q&A Community Based on Word2Vec-CNN
  crossref Yanni Yang, Yiting Tan, Yang Yang, Zhengwei Huang 2024-03-19T18:09:52Z 置信度 0.70
• The Circuit Realization of a Neuromorphic Computing System with Memristor-Based Synapse Design
  crossref Beiye Liu, Yiran Chen, Bryant Wysocki, Tingwen Huang 2012-11-05T08:33:26Z 置信度 0.70
• (Digital Presentation) Oxide Memristors Based on SiO₂ with Cu/Ag Alloy Metallization for Neuromorphic Computing
  By mimicking biomimetic synaptic processes, the success of artificial intelligence (AI) has been astounding with various applications such as driving automation, big data analysis, and natural-language processing.[1-4] Due to a large quantity of data transmiss…
  crossref Fei Qin, Han Wook Song 2022-11-23T19:57:33Z 置信度 0.70
• Dendritic Computation for Neuromorphic Applications
  crossref Suma Cardwell, Frances Chance 2024-09-11T20:52:57Z 置信度 0.70
• A Neuromorphic Radar Sensor for Low-Power IoT Systems
  As radar sensors become an integral component of Internet of Things (IoT) systems, the challenge of high power consumption poses a significant barrier, especially for battery-operated devices. This article introduces NeuroRadar, a groundbreaking solution that …
  crossref Kai Zheng, Kun Qian, Timothy Woodford, Xinyu Zhang 2024-10-22T22:26:35Z 置信度 0.70
• Exponential Synchronization of Nonlinear Interconnected Reaction-Diffusion Memristive NNs under Stochastic Cyber Attacks via Pointwise Control
  crossref Xiaona Song, Jingtao Man, Shuai Song 2021-11-29T20:59:32Z 置信度 0.70
• Three dimensional memristor-based neuromorphic computing system and its application to cloud robotics
  crossref Hongyu An, Jialing Li, Ying Li, Xin Fu 等 2017-08-01T23:00:35Z 置信度 0.70
• Memristive Artificial Synapses for Neuromorphic Computing
  Abstract Neuromorphic computing simulates the operation of biological brain function for information processing and can potentially solve the bottleneck of the von Neumann architecture. This computing is realized based on memristive hardware neural networks in…
  crossref Wen Huang, Xuwen Xia, Chen Zhu, Parker Steichen 等 2021-03-06T07:03:03Z 置信度 0.70
• Neuromorphic Computing in the Era of Large Models
  crossref Haoxuan SHAN, Chiyue WEI, Nicolas RAMOS, Xiaoxuan YANG 等 2025-04-30T23:26:44Z 置信度 0.70
• Parameter estimation of fractional-order memristor-based chaotic systems using state transition algorithm
  crossref Zhaoke Huang, Xiaojun Zhou 2021-11-29T20:59:32Z 置信度 0.70
• Efficient AI Systems through Neuromorphic Computing: Bridging Biological Intelligence and Machine Learning
  The growing computational requirement of artificial intelligence (AI) mock-ups creates challenges in energy efficacy, scalability, and inactivity. Traditional AI architectures built on von Neumann structures and experience integral restrictions in imitating th…
  crossref Aparna Sharma, Bhavesh Kumar Sharma, Shashi Bhushan, Vishvendra Pal Singh Nagar 等 2026-03-30T20:03:06Z 置信度 0.70
• SPINDLE
  crossref Shankar Ganesh Ramasubramanian, Rangharajan Venkatesan, Mrigank Sharad, Kaushik Roy 等 2014-08-01T20:13:39Z 置信度 0.70
• Memristor-Based Neural Network Circuit of Long-term Memory
  crossref Juntao Han, Junwei Sun, Xiao Xiao, Peng Liu 2021-11-29T20:59:32Z 置信度 0.70
• Ferroelectric Schottky Barrier MOSFET as Analog Synapses for Neuromorphic Computing
  crossref Fengben Xi, Andreas Grenmy, Jiayuan Zhang, Yi Han 等 2022-11-03T21:58:34Z 置信度 0.70
• Neuromorphic Visual Artificial Synapse In-Memory Computing Systems Based on GeOx-Coated MXene Nanosheets
  crossref Tianshi Zhao, Yixin Cao, Chenguang Liu, Chun Zhao 等 2023-01-30T16:07:47Z 置信度 0.70
• Recent advances in fluidic neuromorphic computing
  Human brain is capable of optimizing information flow and processing without energy-intensive data shuttling between processor and memory. At the core of this unique capability are billions of neurons connected through trillions of synapses—basic processing un…
  crossref Cheryl Suwen Law, Juan Wang, Kornelius Nielsch, Andrew D. Abell 等 2025-04-24T12:34:38Z 置信度 0.70
• Feedforward spiking neural networks are not transformers (yet): a learning-theoretic framework for long-range dependencies and biological efficiency
  Abstract Spiking neural networks offer a promising route toward low-power sequence computation on neuromorphic hardware, but they continue to lag behind attention-based artificial neural networks on long-context tasks. A central open question is whether this g…
  crossref William Fishell, Gord Fishell, Suraj Honnuraiah 2026-07-03T22:52:23Z 置信度 0.70
• Bearing-Based Formation Control Using Reset Protocol
  crossref Armand Gires Fomekong Lontchi, Yinglun Mo, Wenfeng Hu 2025-05-19T17:52:05Z 置信度 0.70
• MindCore: Spike-Driven Programmable Accelerator for On-Device Neuromorphic Computing
  crossref Hawon Park, Si Yong Lee, Ryangjin Lee, Yoora Kim 等 2025-12-31T18:41:58Z 置信度 0.70
• Live demonstration: Real-time image classification on a neuromorphic computing system with zero off-chip memory access
  crossref Taehwan Shin, Yongshin Kang, Seungho Yang, Seban Kim 等 2016-11-01T20:59:26Z 置信度 0.70
• Neuromorphic Edge Computing: Challenges, Opportunities, and Current Solutions
  crossref Federico Corradi, Amir Zjajo, Letícia Maria Bolzani Poehls, Miloš Krstić 等 2025-12-03T18:39:13Z 置信度 0.70
• Attack Time Consensus Control for 3D Multi-Missile Systems Based on Adaptive Dynamic Programming
  crossref Fenglan Sun, Xinwei Yang, Jiashuo Su, Jiaoyan Zhu 2025-05-19T17:52:05Z 置信度 0.70
• Emerging Opportunities for 2D Materials in Neuromorphic Computing
  Recently, two-dimensional (2D) materials and their heterostructures have been recognized as the foundation for future brain-like neuromorphic computing devices. Two-dimensional materials possess unique characteristics such as near-atomic thickness, dangling-bo…
  crossref Chenyin Feng, Wenwei Wu, Huidi Liu, Junke Wang 等 2023-10-07T14:17:26Z 置信度 0.70
• A memristive diode for neuromorphic computing
  crossref Xiaolei Wang, Qi Shao, Pui Sze Ku, Antonio Ruotolo 2014-12-24T12:01:26Z 置信度 0.70
• EEG Channel Selection Based on Neuron Proportion with SNN for Motor Imagery Classification
  crossref Zhihui Sun, Chaoqiong Fan, Tianyuan Jia, Qing Li 等 2024-03-19T18:09:52Z 置信度 0.70
• Neuromorphic Circuits With Spiking Astrocytes for Increased Energy Efficiency, Fault Tolerance, and Memory Capacitance
  crossref Aybars Yunusoglu, Dexter Le, Murat Isik, I. Can Dikmen 等 2025-09-08T17:38:33Z 置信度 0.70
• Toward a Resilient Future: The Path Ahead
  crossref Ali Akbar Firoozi, Ali Asghar Firoozi 2024-07-26T13:01:53Z 置信度 0.70
• Spintronic Device Potential in Neuromorphic Computing from a Device-Circuit Modeling Perspective
  crossref Bashetty Suman, N M Deepika, B Rajalakshm, Jay Singh 等 2024-03-13T14:55:26Z 置信度 0.70
• Molybdenum Disulfide Memristors for Next Generation Memory and Neuromorphic Computing: Progress and Prospects
  Abstract In the last 15 years memristors have been investigated as devices for high‐density, low‐power, non‐volatile, resistive random access memory (ReRAM) beyond Moore's law. They also show potential in neuromorphic logic architectures to overcome the Von–Ne…
  crossref R. A. Wells, A. W. Robertson 2024-06-04T05:51:29Z 置信度 0.70
• Processing EMG signals using reservoir computing on an event-based neuromorphic system
  crossref Elisa Donati, Melika Payvand, Nicoletta Risi, Renate Krause 等 2018-12-24T23:48:58Z 置信度 0.70
• Learning Accuracy Analysis of Memristor-based Nonlinear Computing Module on Long Short-term Memory
  crossref Hongyu An, Mohammad Shah Al-Mamun, Marius K. Orlowski, Yang Yi 2018-07-26T11:58:06Z 置信度 0.70
• Oxide semiconductor-based ferroelectric thin-film transistors for advanced neuromorphic computing
  Neuromorphic computing that mimics the biological brain has been demonstrated as a next-generation computing method due to its low power consumption and parallel data processing characteristics. To realize neuromorphic computing, diverse neural networks such a…
  crossref Min-Kyu Kim, Ik-Jyae Kim, Jang-Sik Lee 2021-01-19T11:47:10Z 置信度 0.70
• The Future of Holistic Neural Interfaces: 2D Materials, Neuromorphic Computing, and Computational Co-Design
  crossref M. Wilson, M. Ramezani, J. Kim, D. Kuzum 2023-01-23T20:03:43Z 置信度 0.70
• Silicon Photonics for Artificial Intelligence and Neuromorphic Computing
  crossref Bhavin J. Shastri, Thomas Ferreira de Lima, Chaoran Huang, Bicky A. Marquez 等 2021-08-12T20:30:35Z 置信度 0.70
• Pharmacy Science and Neurological Drug Discovery
  In this chapter, we set out on a journey to explore the complex relationship between pharmaceutical science, computational drug discovery, and the world of natural food products. We will deeply investigate the crucial role of food components in shaping the lan…
  crossref Neha Tanwar, Sandeep Kumar, Deepika Verma 2024-11-29T15:16:27Z 置信度 0.70
• Motivation and Challenges for Applying Photonic Neuromorphic Computing Technologies
  crossref B.J. Offrein, E.A. Vlieg, F. Hermann, L. Carraria Martinotti 等 2023-09-04T13:49:33Z 置信度 0.70
• Enabling Neuromorphic Computing: BEOL Integration of CMOS RRAM Chip and Programmable Performance
  crossref Weijie Wang, Victor Yi-Qqian Zhuo, Zhixian Chen, Hock Koon Lee 等 2020-05-07T21:27:28Z 置信度 0.70
• Complexity Degrees of Real Computability
  crossref 2024-11-26T02:06:27Z 置信度 0.70
• Information Transfer in Neuronal Circuits: From Biological Neurons to Neuromorphic Electronics
  The advent of neuromorphic electronics is increasingly revolutionizing the concept of computation. In the last decade, several studies have shown how materials, architectures, and neuromorphic devices can be leveraged to achieve brain-like computation with lim…
  crossref Daniela Gandolfi, Lorenzo Benatti, Tommaso Zanotti, Giulia M. Boiani 等 2024-01-09T07:47:09Z 置信度 0.70
• Slow Electronics and Attractor
  crossref Kantaro Fujiwara 2025-10-31T11:42:52Z 置信度 0.70
• DPSNN: spiking neural network for low-latency streaming speech enhancement
  Abstract Speech enhancement improves communication in noisy environments, affecting areas such as automatic speech recognition (ASR), hearing aids, and telecommunications. With these domains typically being power-constrained and event-based, and often requirin…
  crossref Tao Sun, Sander Bohté 2024-11-18T22:52:15Z 置信度 0.70
• Development of Crosspoint Memory Arrays for Neuromorphic Computing
  Abstract Memristor-based hardware accelerators play a crucial role in achieving energy-efficient big data processing and artificial intelligence, overcoming the limitations of traditional von Neumann architectures. Resistive-switching memories (RRAMs) combine …
  crossref Saverio Ricci, Piergiulio Mannocci, Matteo Farronato, Alessandro Milozzi 等 2024-03-19T06:50:21Z 置信度 0.70
• STDP-based Unsupervised Feature Learning using Convolution-over-time in Spiking Neural Networks for Energy-Efficient Neuromorphic Computing
  Brain-inspired learning models attempt to mimic the computations performed in the neurons and synapses constituting the human brain to achieve its efficiency in cognitive tasks. In this work, we propose Spike Timing Dependent Plasticity-based unsupervised feat…
  crossref Gopalakrishnan Srinivasan, Priyadarshini Panda, Kaushik Roy 2018-11-27T13:18:59Z 置信度 0.70
• Event-Based Imaging for High-Speed Object Detection: A Neuromorphic Computing Approach
  crossref Yogesh Kumar Sharma, Nirmla Sharma, Sameera Iqbal Muhmmad Iqbal, Munish Kumar 等 2025-09-24T17:32:03Z 置信度 0.70
• Author response for "Optically/Electrically Controlled Ag<sup>+</sup> Metallization in Solution-Processed Oxide Memtransistors for Neuromorphic Computing"
  crossref Rajarshi Chakraborty, Himanshu Singodia, Subarna Pramanik, Akhilesh Kumar Yadav 等 2025-10-10T21:02:35Z 置信度 0.70
• Parasitic Extraction and Signal Integrity Analysis of Memristor-Based Crossbar Arrays for Neuromorphic Computing
  crossref Tahsin Binte Shameem, Hanzhi Ma, Yi Zhou, Zohreh Salehi 等 2025-06-26T13:40:11Z 置信度 0.70
• Neuromorphic Photonic Devices and Applications
  crossref 2024-01-21T08:58:46Z 置信度 0.70
• Emulations of synaptic plasticity in the planar transistor configuration for neuromorphic computing
  crossref Yan CHEN, ChenXi ZHANG, LaiYuan WANG, TengFei LI 等 2017-10-17T01:51:20Z 置信度 0.70
• Multi-Sensing Data Fusion for Human Activity Recognition based on Neuromorphic Computing
  crossref Zheqi Yu, Adnan Zahid, William Taylor, Hadi Heidari 等 2022-02-10T20:28:35Z 置信度 0.70
• Toward a Sparse Self-Organizing Map for Neuromorphic Architectures
  Neurobiological systems have often been a source of inspiration for computational science and engineering, but in the past their impact has also been limited by the understanding of biological models. Today, new technologies lead to an equilibrium situation wh…
  crossref Laurent Rodriguez, Benoît Miramond, Bertrand Granado 2015-04-28T12:43:57Z 置信度 0.70
• Multi-Fidelity Nonideality Simulation and Evaluation Framework for Resistive Neuromorphic Computing
  crossref Chenghao Quan, Mohammed E. Fouda, Sugil Lee, Jongeun Lee 2023-03-07T18:41:29Z 置信度 0.70
• A binary classifier based on a reconfigurable dense network of metallic nanojunctions
  Abstract Major efforts to reproduce the brain performances in terms of classification and pattern recognition have been focussed on the development of artificial neuromorphic systems based on top-down lithographic technologies typical of highly integrated comp…
  crossref Matteo Mirigliano, Bruno Paroli, Gianluca Martini, Marco Fedrizzi 等 2021-09-24T22:11:18Z 置信度 0.70
• Low-Power Resistive Switching Characteristics in TiN/TaON/SiO₂/Pt RRAM devices for Neuromorphic Applications
  crossref Ao Yu, Yuechi Ma, Zehao Wang, Xiangxiang Ding 等 2020-04-27T21:34:50Z 置信度 0.70
• Spatiotemporal radar gesture recognition with hybrid spiking neural networks: balancing accuracy and efficiency
  Abstract Radar-based human activity recognition (HAR) offers privacy and robustness over camera-based methods, yet remains computationally demanding for edge deployment. We present the first application of spiking neural networks (SNNs) for radar-based HAR on …
  crossref Riccardo Mazzieri, Eleonora Cicciarella, Jacopo Pegoraro, Federico Corradi 等 2026-02-16T23:07:23Z 置信度 0.70
• A Novel and Efficient Block-Based Programming for ReRAM-Based Neuromorphic Computing
  crossref Wei-Lun Chen, Fang-Yi Gu, Ing-Chao Lin, Grace Li Zhang 等 2023-11-30T18:58:45Z 置信度 0.70
• Neuromorphic Processors with Memristive Synapses
  Due to their nonvolatile nature, excellent scalability, and high density, memristive nanodevices provide a promising solution for low-cost on-chip storage. Integrating memristor-based synaptic crossbars into digital neuromorphic processors (DNPs) may facilitat…
  crossref Qian Wang, Yongtae Kim, Peng Li 2016-05-13T14:30:58Z 置信度 0.70
• GreenMorph: Sustainable Neuromorphic Computing through Energy-Harvesting and Energy-Driven Online STDP Learning
  crossref Yuga Hanyu, Subbaiah Ravi Hariprakash, Abderazek Ben Abdallah, Zhishang Wang 等 2026-06-18T20:06:41Z 置信度 0.70
• An efficient temperature dependent compact model for nanosheet FET for neuromorphic computing circuit
  crossref N. Aruna Kumari, Abhishek Kumar Upadhyay, Vikas Vijayvargiya, Gaurav Singh 等 2025-03-10T16:10:59Z 置信度 0.70
• SuperMind: a survey of the potential of superconducting electronics for neuromorphic computing
  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 co…
  crossref Michael Schneider, Emily Toomey, Graham Rowlands, Jeff Shainline 等 2022-01-19T17:12:31Z 置信度 0.70
• Drone Virtual Fence Using a Neuromorphic Camera
  crossref Terrence Stewart, Marc-Antoine Drouin, Guillaume Gagne, Guy Godin 2021-10-13T14:38:20Z 置信度 0.70
• Deep Learning Techniques for Wireless Communication Systems
  crossref Ziad El-Khatib, Sherif Moussa 2025-01-15T07:23:10Z 置信度 0.70
• The Lynchpin of In-Memory Computing: A Benchmarking Framework for Vector-Matrix Multiplication in RRAMs
  crossref Md Tawsif Rahman Chowdhury, Huynh Quang Nguyen Vo, Paritosh Ramanan, Murat Yildirim 等 2024-12-02T18:37:03Z 置信度 0.70
• A Study of the Comparison between Artificial Neural Networks, Logistic Regression and Similarity Weighted Instance-Based Learning in Modeling and Predicting Trends in Deforestation
  The change in forest cover plays a vital role in ecosystem services, atmospheric carbon balance, and, thus, climate change. In this study, land use maps for the periods 1984 and 2012, derived from Landsat TM satellite imagery, were used. The goal of this study…
  crossref Zeynab Moradi, Ali Reza Mikaeili Tabrizi 2023-05-31T14:26:32Z 置信度 0.70