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• Live Demonstration: A Personalized sEMG Gesture Recognition System Based on On-Chip Learning Neuromorphic Processor
  crossref Xijie Li, Jilin Zhang, Hong Chen 2026-06-18T20:06:41Z 置信度 0.70
• TestuNose: A Spike-Time-Based Olfactory System That Sniffs
  crossref Timothy Horiuchi, Gerald Lu, Sai Ryan 2026-07-16T05:57:39Z 置信度 0.70
• Bio-Inspired Variance Control: Decoupling Signaling Mode from Power Constraints in Neuromorphic Systems
  crossref Rakesh Sengupta 2026-04-07T19:55:00Z 置信度 0.70
• Near-Infrared Circular-Polarization-Sensitive Chiral Au Metasurface/MoS2 Neuromorphic Device for Polarization Encryption
  Near-infrared circularly polarized light, with strong penetration capability, low background interference, and helicity freedom, holds great promise for polarization imaging, information encryption, near-infrared communication, and neuromorphic perception. Her…
  crossref Ming Huang, Wajid Ali, Wanying Li, Lei Wang 等 2026-07-10T01:43:17Z 置信度 0.70
• Toward Trustworthy Neuromorphic <scp>AI</scp> : A Bayesian Framework for Uncertainty‐Aware Spiking Neural Networks
  ABSTRACT Spiking Neural Networks (SNNs) offer a biologically plausible and energy‐efficient paradigm for processing temporal data, particularly suited for neuromorphic computing platforms. However, their deterministic nature limits their deployment in safety‐c…
  crossref Solomon Mamo Banteywalu, Paul Leroux 2026-03-08T09:27:40Z 置信度 0.70
• Nanoionic neuromorphic devices
  crossref Hao TIAN, Haoxuan JIAO, Yuzhi FANG, Ruoyu TONG 等 2026-04-07T02:40:09Z 置信度 0.70
• A constrained RESET–based filament strengthening approach for analog weight control in neuromorphic ReRAM
  crossref Aaron DiFilippo, Shehla Yasmeen, Marius Orlowski 2026-05-21T16:20:03Z 置信度 0.70
• Unveiling the role of defect clusters in neuromorphic systems for ferroelectric thin films: a phase-field study
  crossref Li LI, Minmin ZHU, Chenxi WANG 2026-04-17T09:13:22Z 置信度 0.70
• How can neuromorphic hardware attain brain-like functional capabilities?
  The author provides 4 design principles of how to make cortical microcircuits into neuromorphic hardwares, shedding light for the next generation neuromorphic hardware design.
  crossref Wolfgang Maass 2023-11-30T16:51:35Z 置信度 0.70
• Reconfigurable, non-volatile neuromorphic photovoltaics
  Abstract Reconfigurable image sensors for the recognition and understanding of real-world objects are now becoming an essential part of machine vision technology. The neural network image sensor — which mimics neurobiological functions of the human retina —has…
  crossref Tangxin Li, Jinshui Miao, Xiao Fu, Bo Song 等 2023-02-21T17:14:34Z 置信度 0.70
• A Review of Current Neuromorphic Approaches for Vision, Auditory, and Olfactory Sensors
  crossref Anup Vanarse, Adam Osseiran, Alexander Rassau 2016-03-29T03:14:04Z 置信度 0.70
• Perspective: A review on memristive hardware for neuromorphic computation
  Neuromorphic computation is one of the axes of parallel distributed processing, and memristor-based synaptic weight is considered as a key component of this type of computation. However, the material properties of memristors, including material related physics…
  crossref Changhyuck Sung, Hyunsang Hwang, In Kyeong Yoo 2018-10-05T10:52:49Z 置信度 0.70
• Noise-driven neuromorphic tuned amplifier
  crossref Duccio Fanelli, Francesco Ginelli, Roberto Livi, Niccoló Zagli 等 2017-12-26T15:05:34Z 置信度 0.70
• Review of nanoscale memristor devices as synapses in neuromorphic systems
  crossref Nathan Serafino, Mona Zaghloul 2013-12-04T21:14:10Z 置信度 0.70
• A physical emulation of somatosensory cortex as a Neuromorphic Twin for neural prostheses
  Abstract Loss of tactile feedback in patients with paralysis or limb loss prevents the integration of sensory-motor signals, limiting dexterous motor control. Recent advances in the neurostimulation of the human somatosensory cortex offer the possibility of re…
  crossref Hector Ramirez, Elisa Donati, Wolfger von der Behrens, Giacomo Indiveri 等 2024-05-14T00:51:51Z 置信度 0.70
• Neuromorphic Analogue VLSI
  crossref R Douglas, M Mahowald, C Mead 2003-07-02T07:43:55Z 置信度 0.70
• Toward brain-inspired intelligence: a review of photonic neuromorphic computing systems
  Abstract Inspired by the key elements and principles in the brain, photonic neuromorphic computing shows great potential for building next-generation intelligent processing systems with high parallelism, low latency, low power consumption, and self-learning ca…
  crossref Dun Lan, Bowen Ma, Yuxiang Ji, Yichen Zeng 等 2026-08-28T03:30:07Z 置信度 0.70
• Privacy-Preserving Federated Spiking Neural Networks for Real-Time Target Detection in Distributed ISAC Edge Systems
  This study proposes a privacy-preserving federated spiking neural network (SNN) framework for real-time target detection in integrated sensing and communication (ISAC) edge networks. The framework enables distributed vehicular nodes operating at 28 GHz to coll…
  datacite Mohammad Zahangir Alam 2026 置信度 0.66
• A neuromorphic electronic artist for robotic painting
  Recent advances in deep learning have sparked interest in AI-generated art, including robot-assisted painting. Traditional painting machines use static images and offline processing without considering the dynamic nature of painting. Neuromorphic cameras, whic…
  datacite Schürmann, Lioba, D'Angelo, Giulia, Grayver, Liat, Bartolozzi, Chiara 等 2025 置信度 0.66 Computer scienceElectrical and electronic engeineering
• EFLOP: a sparsity-aware metric for evaluating computational cost in spiking and non-spiking neural networks
  Deploying energy-efficient deep neural networks on energy-constrained edge devices is an important research topic in both machine learning and circuit design communities. Both artificial neural networks (ANNs) and spiking neural networks (SNNs) have been propo…
  datacite Narduzzi, Simon, Zenke, Friedemann, Liu, Shih-Chii, Dunbar, L. Andrea 2025 置信度 0.66 computational costeffective floating-point operationspruningsparsity-aware trainingrecurrent neural networks
• Stable recurrent dynamics in heterogeneous neuromorphic computing systems using excitatory and inhibitory plasticity
  Many neural computations emerge from self-sustained patterns of activity in recurrent neural circuits, which rely on balanced excitation and inhibition. Neuromorphic electronic circuits represent a promising approach for implementing the brain’s computational …
  datacite Soldado-Magraner, Saray, Sorbaro, Martino, Laje, Rodrigo, Buonomano, Dean V. 等 2025 置信度 0.66 Electrical and electronic engineeringLearning algorithms
• A Realistic Simulation Framework for Analog/Digital Neuromorphic Architectures
  Developing dedicated mixed-signal neuromorphic computing systems optimized for real-time sensory-processing in extreme edge-computing applications requires time-consuming design, fabrication, and deployment of full-custom neuromorphic processors. To ensure tha…
  datacite Quintana, Fernando M., , Maryada, Galindo, Pedro L., Donati, Elisa 等 2025 置信度 0.66 SNNDPIneuromorphicPyTorchDYNAP-SE
• Genetic Motifs as a Blueprint for Mismatch-Tolerant Neuromorphic Computing
  Mixed-signal implementations of SNNs offer a promising solution to edge computing applications that require low-power and compact embedded processing systems. However, device mismatch in the analog circuits of these neuromorphic processors poses a significant …
  datacite Boccato, Tommaso, Zendrikov, Dmitrii, Toschi, Nicola, Indiveri, Giacomo 2024 置信度 0.66 Spiking neural networksMixed-signal chipsDevice mismatchNetwork neuroscience
• Commentary: Accelerating spiking neural network simulations with PymoNNto and PymoNNtorch
  Frontiers in neuroinformatics 18, 1446620 (2024). doi:10.3389/fninf.2024.1446620
  datacite Plesser, Hans Ekkehard 2024 置信度 0.66 610
• Neuromorphic dreaming as a pathway to efficient learning in artificial agents
  The computational substrate of biological systems exhibits remarkable abilities to learn complex skills quickly and efficiently. Inspired by this, we implement model-based reinforcement learning using spiking neural networks directly on mixed-signal neuromorph…
  datacite Blakowski, Ingo, Zendrikov, Dmitrii, Indiveri, Giacomo, Capone, Cristiano 2025 置信度 0.66 neuromorphic computingspiking neural networksmodel-based reinforcement learningoffline learningsample efficiency
• Recurrent models of orientation selectivity enable robust early-vision processing in mixed-signal neuromorphic hardware
  Mixed signal analog/digital neuromorphic circuits represent an ideal medium for reproducing bio-physically realistic dynamics of biological neural systems in real-time. However, similar to their biological counterparts, these circuits have limited resolution a…
  datacite Baruzzi, Valentina, Indiveri, Giacomo, Sabatini, Silvio P. 2025 置信度 0.66
• Event driven neural network on a mixed signal neuromorphic processor for EEG based epileptic seizure detection
  Long-term monitoring of biomedical signals is essential for the modern clinical management of neurological conditions such as epilepsy. However, developing wearable systems that are able to monitor, analyze, and detect epileptic seizures with long-lasting oper…
  datacite Bartels, Jim, Gallou, Olympia, Ito, Hiroyuki, Cook, Matthew 等 2025 置信度 0.66
• Spiking Neural Networks for Radio Frequency Interference Detection in Radio Astronomy
  Spiking Neural Networks (SNNs) promise efficient and dynamic spatio-temporal data processing. This paper reformulates a significant challenge in radio astronomy, Radio Frequency Interference (RFI) detection, as a time-series segmentation task suited for SNN ex…
  datacite Pritchard, Nicholas J., Wicenec, Andreas, Bennamoun, Mohammed, Dodson, Richard 2024 置信度 0.66 Neural and Evolutionary Computing (cs.NE)Instrumentation and Methods for Astrophysics (astro-ph.IM)FOS: Computer and information sciencesFOS: Computer and information sciencesFOS: Physical sciences
• CHANI: Correlation-based Hawkes Aggregation of Neurons with bio-Inspiration
  The present work aims at proving mathematically that a neural network inspired by biology can learn a classification task thanks to local transformations only. In this purpose, we propose a spiking neural network named CHANI (Correlation-based Hawkes Aggregati…
  datacite Jaffard, Sophie, Vaiter, Samuel, Reynaud-Bouret, Patricia 2024 置信度 0.66 Statistics Theory (math.ST)Machine Learning (stat.ML)FOS: MathematicsFOS: MathematicsFOS: Computer and information sciences
• Event-based Heterogeneous Information Processing for Online Vision-based Obstacle Detection and Localization
  This paper introduces a novel framework for robotic vision-based navigation that integrates Hybrid Neural Networks (HNNs) with Spiking Neural Network (SNN)-based filtering to enhance situational awareness for unmodeled obstacle detection and localization. By l…
  datacite Ahmadvand, Reza, Sharif, Sarah Safura, Banad, Yaser Mike 2026 置信度 0.66 Robotics (cs.RO)Computer Vision and Pattern Recognition (cs.CV)FOS: Computer and information sciencesFOS: Computer and information sciences
• Biological Intuition on Digital Hardware: An RTL Implementation of Poisson-Encoded SNNs for Static Image Classification
  The deployment of Artificial Intelligence on edge devices (TinyML) is often constrained by the high power consumption and latency associated with traditional Artificial Neural Networks (ANNs) and their reliance on intensive Matrix-Multiply (MAC) operations. Ne…
  datacite Das, Debabrata, K., Yogeeth G., Gupta, Arnav 2026 置信度 0.66 Hardware Architecture (cs.AR)FOS: Computer and information sciencesFOS: Computer and information sciencesB.7.1, C.1.3, I.2.6
• SNN-Comprypto: Spiking Neural Network-based Simultaneous Compression and Encryption Using Chaotic Reservoir Dynamics
  I propose SNN-Comprypto, a novel cryptographic system that leverages the chaotic dynamics of Spiking Neural Networks (SNNs) to perform simultaneous data compression and encryption. Unlike conventional methods that treat compression and encryption as separate p…
  datacite Funasaki, Hiroto 2026 置信度 0.66 Spiking Neural NetworksCryptographyReservoir Computing
• Hybrid Spiking Language Model: Combining Spike Counts and Membrane Potentials for Energy-Efficient and Noise-Robust Character Prediction
  I propose a novel character-level language model using Spiking Neural Networks (SNNs) that combines both spike counts and membrane potentials for output prediction. This v3 extends the model with BitNet ternary weights and RWKV-inspired time-mixing. Key Findin…
  datacite Funasaki, Hiroto 2026 置信度 0.66 Spiking Neural NetworkLanguage ModelNeuromorphic ComputingEnergy EfficiencyNoise Robustness
• Adaptive Neural Continuity Protocol: Real-Time Compensation for Progressive Hippocampal Neurodegeneration
  This research presents the Continuity Protocol, a computational framework designed to preserve cognitive information integrity during progressive hippocampal decay. Using a 1,500-neuron spiking neural network in the Nengo environment, we demonstrate real-time …
  datacite Farag, Mina K. 2026 置信度 0.66 Computational NeuroscienceNengoNEFSpiking Neural NetworksHippocampus
• Adaptive Neural Continuity Protocol: Real-Time Compensation for Progressive Hippocampal Neurodegeneration
  This research presents the Continuity Protocol, a computational framework designed to preserve cognitive information integrity during progressive hippocampal decay. Using a 1,500-neuron spiking neural network in the Nengo environment, we demonstrate real-time …
  datacite Farag, Mina K. 2026 置信度 0.66 Computational NeuroscienceNengoNEFSpiking Neural NetworksHippocampus
• Bioinspired spiking architecture enables energy constrained touch encoding
  This capsule enables the reproduction of the main results presented in the paper: "Bioinspired Spiking Architecture Enables Energy-Constrained Touch Encoding".
  datacite Ortone, Andrea, Filosa, Mariangela, Indiveri, Giacomo, Desoli, Giuseppe 等 2026 置信度 0.66 CapsuleEngineeringTactile PerceptionSpiking neural networkenergy efficient AI
• Functions of spiking neural networks constrained by biology
  Artificial intelligence (AI) solutions are increasingly taking on tasks traditionally performed by humans. However, their rising computational demands and energy consumption are unsustainable, highlighting the need for more efficient designs. The human brain, …
  datacite Korcsak-Gorzo, Agnes 2025 置信度 0.66
• Effects of Introducing Synaptic Scaling on Spiking Neural Network Learning
  Spiking neural networks (SNNs) employing unsupervised learning methods inspired by neural plasticity are expected to be a new framework for artificial intelligence. In this study, we investigated the effect of multiple types of neural plasticity, such as spike…
  datacite Touda, Shinnosuke, Okuno, Hirotsugu 2026 置信度 0.66 Neural and Evolutionary Computing (cs.NE)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• Hybrid Spiking Language Model: Combining Spike Counts and Membrane Potentials for Energy-Efficient and Noise-Robust Character Prediction
  I propose a novel character-level language model using Spiking Neural Networks (SNNs) that combines both spike counts and membrane potentials for output prediction. Unlike conventional SNN approaches that only use spike counts, this hybrid method leverages the…
  datacite Funasaki, Hiroto 2026 置信度 0.66 Spiking Neural NetworkLanguage ModelNeuromorphic ComputingEnergy EfficiencyNoise Robustness
• Brain Inspired Computing Models and Architectures
  With an exponential increase in the amount of data collected per day, the fields of artificial intelligence and machinelearning continue to progress at a rapid pace with respect to algorithms, models, applications, and hardware. In particular,deep neural netwo…
  datacite Diksha Katyal, Kalpana Choudhary, Dimpy Singh 2026 置信度 0.66
• Brain Inspired Computing Models and Architectures
  With an exponential increase in the amount of data collected per day, the fields of artificial intelligence and machinelearning continue to progress at a rapid pace with respect to algorithms, models, applications, and hardware. In particular,deep neural netwo…
  datacite Diksha Katyal, Kalpana Choudhary, Dimpy Singh 2026 置信度 0.66
• Hybrid Spiking Language Model: Combining Spike Counts and Membrane Potentials for Energy-Efficient and Noise-Robust Character Prediction
  I propose a novel character-level language model using Spiking Neural Networks (SNNs) that combines both spike counts and membrane potentials for output prediction. Unlike conventional SNN approaches that only use spike counts, this hybrid method leverages the…
  datacite Funasaki, Hiroto 2026 置信度 0.66 Spiking Neural NetworkLanguage ModelNeuromorphic ComputingEnergy EfficiencyNoise Robustness
• NeuroMorphic Robotic Brain (NeuRob): A Spiking Neural Network Architecture for Autonomous, Adaptive Robot Control Lifelong Learning in Unstructured Environments with SNNs: A Theoretical Approach
  datacite Karmakar, Rupam Kumar 2026 置信度 0.66 Computer EngineeringEngineering
• SNN-Comprypto: Spiking Neural Network-based Simultaneous Compression and Encryption Using Chaotic Reservoir Dynamics
  We propose SNN-Comprypto, a novel cryptographic system that leverages the chaotic dynamics of Spiking Neural Networks (SNNs) to perform simultaneous data compression and encryption. Unlike conventional methods that treat compression and encryption as separate …
  datacite Funasaki, Hiroto 2026 置信度 0.66 Spiking Neural NetworksCryptographyReservoir Computing
• SNN-Comprypto: Spiking Neural Network-based Simultaneous Compression and Encryption Using Chaotic Reservoir Dynamics
  We propose SNN-Comprypto, a novel cryptographic system that leverages the chaotic dynamics of Spiking Neural Networks (SNNs) to perform simultaneous data compression and encryption. Unlike conventional methods that treat compression and encryption as separate …
  datacite Funasaki, Hiroto 2026 置信度 0.66 Spiking Neural NetworksCryptographyReservoir Computing
• SNN-Comprypto: Spiking Neural Network-based Simultaneous Compression and Encryption Using Chaotic Reservoir Dynamics
  We propose SNN-Comprypto, a novel cryptographic system that leverages the chaotic dynamics of Spiking Neural Networks (SNNs) to perform simultaneous data compression and encryption. Unlike conventional methods that treat compression and encryption as separate …
  datacite Funasaki, Hiroto 2026 置信度 0.66 Spiking Neural NetworksCryptographyReservoir Computing
• Macroscopic dynamics of quadratic integrate-and-fire neurons subject to correlated noise
  The presence of correlated noise, arising from a mixture of independent fluctuations and a common noisy input shared across the neural population, is a ubiquitous feature of neural circuits, yet its impact on collective network dynamics remains poorly understo…
  datacite Wang, Hui, Zheng, Chunming 2026 置信度 0.66 Neurons and Cognition (q-bio.NC)Statistical Mechanics (cond-mat.stat-mech)Adaptation and Self-Organizing Systems (nlin.AO)FOS: Biological sciencesFOS: Biological sciences
• Conceptual Framework for Adaptive Biohybrid Neural Interfaces: Innovations in Medical Neural Engineering to Address Biocompatibility and Energy Efficiency Challenges
  This conceptual paper proposes an adaptive biohybrid neural interface (ABNI) framework that integrates living immune cells (monocytes) with subcellular-sized wireless photovoltaic electronic devices (SWED) to enable minimally invasive, targeted closed-loop neu…
  datacite Shibah, Sami Rashid Mohammed 2026 置信度 0.66
• Conceptual Framework for Adaptive Biohybrid Neural Interfaces: Innovations in Medical Neural Engineering to Address Biocompatibility and Energy Efficiency Challenges
  This conceptual paper proposes an adaptive biohybrid neural interface (ABNI) framework that integrates living immune cells (monocytes) with subcellular-sized wireless photovoltaic electronic devices (SWED) to enable minimally invasive, targeted closed-loop neu…
  datacite Shibah, Sami Rashid Mohammed 2026 置信度 0.66
• AN NLP-BASED APPROACH FOR SARCASM DETECTION IN MARATHI LANGUAGE WITHIN MULTILINGUAL ENVIRONMENTS
  The expansion of social media platforms has required the development of complex natural language processing (NLP) methods for sentiment analysis and sarcasm detection, especially for low-resource languages. This research presents a novel, ensemble-based NLP fr…
  datacite Journal of Theoretical and Applied Information Technology 2026 置信度 0.66 Sarcasm detection, Marathi NLP, Code-Mixed Text, Ensemble Learning, Multilingual Sentiment Analysis, Low-Resource Language
• AN NLP-BASED APPROACH FOR SARCASM DETECTION IN MARATHI LANGUAGE WITHIN MULTILINGUAL ENVIRONMENTS
  The expansion of social media platforms has required the development of complex natural language processing (NLP) methods for sentiment analysis and sarcasm detection, especially for low-resource languages. This research presents a novel, ensemble-based NLP fr…
  datacite Journal of Theoretical and Applied Information Technology 2026 置信度 0.66 Sarcasm detection, Marathi NLP, Code-Mixed Text, Ensemble Learning, Multilingual Sentiment Analysis, Low-Resource Language
• Hybrid guided variational autoencoder for visual place recognition
  Autonomous agents such as cars, robots and drones need to precisely localize themselves in diverse environments, including in GPS-denied indoor environments. One approach for precise localization is visual place recognition (VPR), which estimates the place of …
  datacite Wang, Ni, You, Zihan, Neftci, Emre, Schoepe, Thorben 2026 置信度 0.66 Computer Vision and Pattern Recognition (cs.CV)Artificial Intelligence (cs.AI)FOS: Computer and information sciencesFOS: Computer and information sciences
• SYNtzulu, SNN at the Micro-Edge: The Evolution of the Mosquito.
  SYNtzulu is an ultra-low power Spiking Neural Network inference engine designed for near-sensor processing at the edge. We describe the architectural progression that enables Syntzulu to support online artificial intelligence data analysis locally on a range o…
  datacite Leone, Gianluca 2025 置信度 0.66
• SYNtzulu, SNN at the Micro-Edge: The Evolution of the Mosquito.
  SYNtzulu is an ultra-low power Spiking Neural Network inference engine designed for near-sensor processing at the edge. We describe the architectural progression that enables Syntzulu to support online artificial intelligence data analysis locally on a range o…
  datacite Leone, Gianluca 2025 置信度 0.66
• Stochastic synaptic dynamics under learning
  Learning is based on synaptic plasticity, which affects and is driven by neural activity. Because pre- and postsynaptic spiking activity is shaped by randomness, the synaptic weights follow a stochastic process, requiring a probabilistic framework to capture t…
  datacite Stubenrauch, Jakob, Auer, Naomi, Kempter, Richard, Lindner, Benjamin 2025 置信度 0.66 Neurons and Cognition (q-bio.NC)Disordered Systems and Neural Networks (cond-mat.dis-nn)Biological Physics (physics.bio-ph)FOS: Biological sciencesFOS: Biological sciences
• Bruno: Backpropagation Running Undersampled for Novel device Optimization
  Recent efforts to improve the efficiency of neuromorphic and machine learning systems have centred on developing of specialised hardware for neural networks. These systems typically feature architectures that go beyond the von Neumann model employed in general…
  datacite Fehlings, Luca, Zhang, Bojian, Gibertini, Paolo, Nicholson, Martin A. 等 2025 置信度 0.66 Neural and Evolutionary Computing (cs.NE)Artificial Intelligence (cs.AI)FOS: Computer and information sciencesFOS: Computer and information sciences
• Sleep-Based Homeostatic Regularization for Stabilizing Spike-Timing-Dependent Plasticity in Recurrent Spiking Neural Networks
  Spike-timing-dependent plasticity (STDP) provides a biologically-plausible learning mechanism for spiking neural networks (SNNs); however, Hebbian weight updates in architectures with recurrent connections suffer from pathological weight dynamics: unbounded gr…
  datacite Massey, Andreas, Hubin, Aliaksandr, Nichele, Stefano, Sæbø, Solve 2026 置信度 0.66 Neural and Evolutionary Computing (cs.NE)Machine Learning (stat.ML)FOS: Computer and information sciencesFOS: Computer and information sciences
• A brain-inspired information fusion method for enhancing robot GPS outages navigation
  Low-cost inertial navigation systems (INS) are prone to sensor biases and measurement noise, which lead to rapid degradation of navigation accuracy during global positioning system (GPS) outages. To address this challenge and improve positioning continuity in …
  datacite Liu, Yaohua, Zhang, Hengjun, Ou, Binkai 2026 置信度 0.66 Robotics (cs.RO)FOS: Computer and information sciencesFOS: Computer and information sciences
• Temporal Regularization Training: Unleashing the Potential of Spiking Neural Networks
  Spiking Neural Networks (SNNs) have received widespread attention due to their event-driven and low-power characteristics, making them particularly effective for processing neuromorphic data. Recent studies have shown that directly trained SNNs suffer from sev…
  datacite Zhang, Boxuan, Xu, Zhen, Tao, Kuan 2025 置信度 0.66 Neural and Evolutionary Computing (cs.NE)Artificial Intelligence (cs.AI)FOS: Computer and information sciencesFOS: Computer and information sciences
• Efficient Aspect Term Extraction using Spiking Neural Network
  Aspect Term Extraction (ATE) identifies aspect terms in review sentences, a key subtask of sentiment analysis. While most existing approaches use energy-intensive deep neural networks (DNNs) for ATE as sequence labeling, this paper proposes a more energy-effic…
  datacite Mishra, Abhishek Kumar, Somasundaram, Arya, Das, Anup, Kandasamy, Nagarajan 2026 置信度 0.66 Computation and Language (cs.CL)FOS: Computer and information sciencesFOS: Computer and information sciences
• Anarchy, Nonlinearity, and Emergence in International Relations: A Spiking Neural Network Framework Approach
  This research proposes a novel approach to modeling international relations by leveraging the Spiking Neural Networks (SNN) framework within a systems theory perspective. While existing systems theory methods, including agent-based modeling, network analysis, …
  datacite Wanhong HUANG 2026 置信度 0.66 Dynamical SystemsPhysical Sciences and MathematicsDynamics and Dynamical SystemsEngineering Science and MaterialsMathematics
• Anarchy, Nonlinearity, and Emergence in International Relations: A Spiking Neural Network Framework Approach
  This research proposes a novel approach to modeling international relations by leveraging the Spiking Neural Networks (SNN) framework within a systems theory perspective. While existing systems theory methods, including agent-based modeling, network analysis, …
  datacite Wanhong HUANG 2026 置信度 0.66 Dynamical SystemsPhysical Sciences and MathematicsMathematicsFOS: MathematicsSocial and Behavioral Sciences
• Anarchy, Nonlinearity, and Emergence in International Relations: A Spiking Neural Network Framework Approach
  This research proposes a novel approach to modeling international relations by leveraging the Spiking Neural Networks (SNN) framework within a systems theory perspective. While existing systems theory methods, including agent-based modeling, network analysis, …
  datacite Wanhong HUANG 2026 置信度 0.66
• A Workload-Aware Energy Comparison of STDP and Surrogate-Gradient Spiking Neural Networks on MNIST
  Spiking neural networks (SNNs) are often proposed as an energy-efficient alternative to conventional neural networks due to sparse, event-driven computation and compatibility with neuromorphic hardware. However, comparisons between biologically inspired local …
  datacite Cheng, Cece 2026 置信度 0.66 spiking neural networkBrian2event-driven computation
• A Workload-Aware Energy Comparison of STDP and Surrogate-Gradient Spiking Neural Networks on MNIST
  Spiking neural networks (SNNs) are often proposed as an energy-efficient alternative to conventional neural networks due to sparse, event-driven computation and compatibility with neuromorphic hardware. However, comparisons between biologically inspired local …
  datacite Cheng, Cece 2026 置信度 0.66 spiking neural networkBrian2event-driven computation
• DylanPerdigao/CalibraSNN
  Source code of the paper entitled "CalibraSNN: Fair and Calibrated Convolutional Spiking Neural Network for High-Stakes Industry Applications" published at "IEEE Access" journal.
  datacite Perdigão, Dylan 2025 置信度 0.66 Spiking Neural NetworksNeuromorphic computingNeural network calibrationImbalanced dataFair ML
• CalibraSNN
  Source code of the paper entitled "CalibraSNN: Fair and Calibrated Convolutional Spiking Neural Network for High-Stakes Industry Applications" published at "IEEE Access" journal.
  datacite Perdigão, Dylan 2025 置信度 0.66
• CalibraSNN
  Source code of the paper entitled "CalibraSNN: Fair and Calibrated Convolutional Spiking Neural Network for High-Stakes Industry Applications" published at "IEEE Access" journal.
  datacite Perdigão, Dylan 2025 置信度 0.66
• DylanPerdigao/CalibraSNN
  Source code of the paper entitled "CalibraSNN: Fair and Calibrated Convolutional Spiking Neural Network for High-Stakes Industry Applications" published at "IEEE Access" journal.
  datacite Perdigão, Dylan 2025 置信度 0.66 Spiking Neural NetworksNeuromorphic computingNeural network calibrationImbalanced dataFair ML
• Gridbot
  The ability to orient in an unknown, fast-changing, environment is an unmet challenge for robots but a seamlessly solved problem for the primate brain. This thesis describes the first steps in developing a neuro-inspired “bottom-up” model of the brain’s naviga…
  datacite Tang, Guangzhi 2017 置信度 0.66
• An interactive framework for visually realistic 3D motion synthesis using evolutionarily-trained spiking neural networks
  We present an end-to-end method for capturing the dynamics of 3D human characters and translating them for synthesizing new, visually-realistic motion sequences. Conventional methods employ sophisticated, but generic, control approaches for driving the joints …
  datacite Patil, Aditi 2023 置信度 0.66
• Non-neuronal computational principles for increased performance in brain-inspired networks
  Brain-inspired neural networks promise to bring human-like machine learning and intelligence by exploiting our understanding of how the brain computes. Yet, current theories of brain information processing are solely focused on neuronal cells as the fundamenta…
  datacite Ivanov, Vladimir Alexandrovich 2022 置信度 0.66
• Biologically inspired spiking neural networks for energy-efficient robot learning and control
  Energy-efficient learning and control are becoming increasingly crucial for robots that solve complex real-world tasks with limited onboard resources. Although deep neural networks (DNN) have been successfully applied to robotics, their high energy consumption…
  datacite Tang, Guangzhi 2022 置信度 0.66
• Memory-Guided Unified Hardware Accelerator for Mixed-Precision Scientific Computing
  Recent hardware acceleration advances have enabled powerful specialized accelerators for finite element computations, spiking neural network inference, and sparse tensor operations. However, existing approaches face fundamental limitations: (1) finite element …
  datacite Wang, Chuanzhen, Zhang, Leo, Liu, Eric 2026 置信度 0.66 Hardware Architecture (cs.AR)FOS: Computer and information sciencesFOS: Computer and information sciences
• A deep spiking machine-hearing system for the case of invasive fish species
  (Uploaded by Plazi for the IPBES Invasive Alien Species Assessment) Prolonged and sustained warming of the sea, acidification of surface water and rising of sea levels, creates significant habitat losses, resulting in the proliferation and spread of invasive s…
  datacite Demertzis, Konstantinos, Iliadis, Lazaros, Anezakis, Vardis-Dimitris 2017 置信度 0.66 Chapter 5biodiversityenvironment assessmentIPBESAlien Invasive Species Assessment AIS
• A deep spiking machine-hearing system for the case of invasive fish species
  (Uploaded by Plazi for the IPBES Invasive Alien Species Assessment) Prolonged and sustained warming of the sea, acidification of surface water and rising of sea levels, creates significant habitat losses, resulting in the proliferation and spread of invasive s…
  datacite Demertzis, Konstantinos, Iliadis, Lazaros, Anezakis, Vardis-Dimitris 2017 置信度 0.66 Chapter 5biodiversityenvironment assessmentIPBESAlien Invasive Species Assessment AIS
• Neural Receptive Fields, Stimulus Space Embedding and Effective Geometry of Scale-Free Networks
  Understanding how receptive fields emerge and organize within brain networks and how neural dynamics couple with stimuli space is fundamental to neuroscience. Models often rely on fine-tuning connectivity to match empirical data, which may limit biological pla…
  datacite Tiselko, Vasilii, Gorsky, Alexander, Dabaghian, Yuri 2025 置信度 0.66 Neurons and Cognition (q-bio.NC)FOS: Biological sciencesFOS: Biological sciences
• Recursive Ai with Codette
  1. Introduction Large language models (LLMs) demonstrate impressive linguistic fluency, yet remain fundamentally reactive systems. They do not preserve long-term identity, do not reason recursively over evolving internal states, and lack mechanisms for epistem…
  datacite Harrison, Jonathan 2026 置信度 0.66
• Resolving inconsistent effects of tDCS on learning using a homeostatic structural plasticity model
  Introduction: Transcranial direct current stimulation (tDCS) is increasingly used to modulate motor learning. Current polarity and intensity, electrode montage, and application before or during learning had mixed effects. Both Hebbian and homeostatic plasticit…
  datacite Lu, Han, Normann, Claus, Frase, Lukas, Rotter, Stefan 2025 置信度 0.66 610
• MD-SNN: Membrane Potential-aware Distillation on Quantized Spiking Neural Network
  Spiking Neural Networks (SNNs) offer a promising and energy-efficient alternative to conventional neural networks, thanks to their sparse binary activation. However, they face challenges regarding memory and computation overhead due to complex spatio-temporal …
  datacite Lee, Donghyun, Moitra, Abhishek, Kim, Youngeun, Yin, Ruokai 等 2025 置信度 0.66 Neural and Evolutionary Computing (cs.NE)FOS: Computer and information sciencesFOS: Computer and information sciences
• Enabling Efficient Processing of Spiking Neural Networks with On-Chip Learning on Commodity Neuromorphic Processors for Edge AI Systems
  The rising demand for energy-efficient edge AI systems (e.g., mobile agents/robots) has increased the interest in neuromorphic computing, since it offers ultra-low power/energy AI computation through spiking neural network (SNN) algorithms on neuromorphic proc…
  datacite Putra, Rachmad Vidya Wicaksana, Wickramasinghe, Pasindu, Shafique, Muhammad 2025 置信度 0.66 Neural and Evolutionary Computing (cs.NE)Artificial Intelligence (cs.AI)Hardware Architecture (cs.AR)Machine Learning (cs.LG)FOS: Computer and information sciences
• QSViT: A Methodology for Quantizing Spiking Vision Transformers
  Vision Transformer (ViT)-based models have shown state-of-the-art performance (e.g., accuracy) in vision-based AI tasks. However, realizing their capability in resource-constrained embedded AI systems is challenging due to their inherent large memory footprint…
  datacite Putra, Rachmad Vidya Wicaksana, Iftikhar, Saad, Shafique, Muhammad 2025 置信度 0.66 Neural and Evolutionary Computing (cs.NE)Artificial Intelligence (cs.AI)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• Energy-Efficient Eimeria Parasite Detection Using a Two-Stage Spiking Neural Network Architecture
  Coccidiosis, a disease caused by the Eimeria parasite, represents a major threat to the poultry and rabbit industries, demanding rapid and accurate diagnostic tools. While deep learning models offer high precision, their significant energy consumption limits t…
  datacite García-Vico, Ángel Miguel, Seker, Huseyin, Afzal, Muhammad 2026 置信度 0.66 Neural and Evolutionary Computing (cs.NE)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• ChronoPlastic Spiking Neural Networks
  Spiking neural networks (SNNs) offer a biologically grounded and energy-efficient alternative to conventional neural architectures; however, they struggle with long-range temporal dependencies due to fixed synaptic and membrane time constants. This paper intro…
  datacite Chaudhry, Sarim 2026 置信度 0.66 Neural and Evolutionary Computing (cs.NE)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• Implementation of high-efficiency, lightweight residual spiking neural network processor based on field-programmable gate arrays
  With the development of hardware-optimized deployment of spiking neural networks (SNNs), SNN processors based on field-programmable gate arrays (FPGAs) have become a research hotspot due to their efficiency and flexibility. However, existing methods rely on mu…
  datacite Yue, Hou, Shuiying, Xiang, Tao, Zou, Zhiquan, Huang 等 2026 置信度 0.66 Neural and Evolutionary Computing (cs.NE)FOS: Computer and information sciencesFOS: Computer and information sciences
• Modeling Excitatory–Inhibitory Balance and Network Stability in Motor Control Circuits Using Spiking Neural Networks
  Abstract Motor control in biological systems relies on precise coordination between excitatory and inhibitory neural activity to support stable and flexible movement. Disruptions to excitatory–inhibitory (E–I) balance are associated with pathological motor beh…
  datacite Tambi, Chukwuemeka, Shine, Gerkariah 2019 置信度 0.66
• Modeling Excitatory–Inhibitory Balance and Network Stability in Motor Control Circuits Using Spiking Neural Networks
  Abstract Motor control in biological systems relies on precise coordination between excitatory and inhibitory neural activity to support stable and flexible movement. Disruptions to excitatory–inhibitory (E–I) balance are associated with pathological motor beh…
  datacite Tambi, Chukwuemeka, Shine, Gerkariah 2019 置信度 0.66
• A Workload-Aware Energy Comparison of STDP and Surrogate-Gradient Spiking Neural Networks on MNIST
  Spiking neural networks (SNNs) are often proposed as an energy-efficient alternative to conventional neural networks due to sparse, event-driven computation and compatibility with neuromorphic hardware. However, comparisons between biologically inspired local …
  datacite Cheng, Cece 2026 置信度 0.66 spiking neural networkBrian2event-driven computation
• Combining Neuroplasticity and Neuromorphic Computing: A Paradigm Shift in Artificial Intelligence V2
  This version represents a substantively revised and corrected preprint of the original manuscript “Combining Neuroplasticity and Neuromorphic Computing.” The document has been expanded and refined to address issues of technical completeness, internal consisten…
  datacite MacFarland, Anthony 2026 置信度 0.66 NeuroplasticityNeuromorphic ComputingSpiking Neural Networks (SNNs)Adaptive LearningHomeostatic Plasticity
• Combining Neuroplasticity and Neuromorphic Computing: A Paradigm Shift in Artificial Intelligence V2
  This version represents a substantively revised and corrected preprint of the original manuscript “Combining Neuroplasticity and Neuromorphic Computing.” The document has been expanded and refined to address issues of technical completeness, internal consisten…
  datacite MacFarland, Anthony 2026 置信度 0.66 NeuroplasticityNeuromorphic ComputingSpiking Neural Networks (SNNs)Adaptive LearningHomeostatic Plasticity
• Neuromorphic Cybersecurity with Semi-supervised Lifelong Learning
  Inspired by the brain's hierarchical processing and energy efficiency, this paper presents a Spiking Neural Network (SNN) architecture for lifelong Network Intrusion Detection System (NIDS). The proposed system first employs an efficient static SNN to identify…
  datacite Mia, Md Zesun Ahmed, Bal, Malyaban, Lu, Sen, Nishibuchi, George M. 等 2025 置信度 0.66 Machine Learning (cs.LG)Artificial Intelligence (cs.AI)Emerging Technologies (cs.ET)Neural and Evolutionary Computing (cs.NE)FOS: Computer and information sciences
• SLTNet: Efficient Event-based Semantic Segmentation with Spike-driven Lightweight Transformer-based Networks
  Event-based semantic segmentation has great potential in autonomous driving and robotics due to the advantages of event cameras, such as high dynamic range, low latency, and low power cost. Unfortunately, current artificial neural network (ANN)-based segmentat…
  datacite Long, Xianlei, Zhu, Xiaxin, Guo, Fangming, Zhang, Wanyi 等 2024 置信度 0.66 Computer Vision and Pattern Recognition (cs.CV)Artificial Intelligence (cs.AI)FOS: Computer and information sciencesFOS: Computer and information sciences
• Optical Spiking Neural Networks via Rogue-Wave Statistics
  Optical computing could reduce the energy cost of artificial intelligence by leveraging the parallelism and propagation speed of light. However, implementing nonlinear activation, essential for machine learning, remains challenging in low-power optical systems…
  datacite Kesgin, Bahadır Utku, Durdu, Gülsüm Yaren, Teğin, Uğur 2025 置信度 0.66 Optics (physics.optics)FOS: Physical sciencesFOS: Physical sciences
• Yatrogenesis/OldiesRules: OldiesRules v0.1.1
  🎸 OldiesRules v0.1.1 Revival of Classic Academic Simulators in Rust Cite this software @software{oldiesrules_2024, author = {Molina, Francisco}, title = {OldiesRules: Revival of Classic Academic Simulators in Rust}, version = {0.1.1}, doi = {10.5281/zenodo.18…
  datacite Molina-Burgos, Francisco 2025 置信度 0.66 GENESISNEURONXPPAUTAUTOCOPASI
• Yatrogenesis/OldiesRules: OldiesRules v0.1.1
  🎸 OldiesRules v0.1.1 Revival of Classic Academic Simulators in Rust Cite this software @software{oldiesrules_2024, author = {Molina, Francisco}, title = {OldiesRules: Revival of Classic Academic Simulators in Rust}, version = {0.1.1}, doi = {10.5281/zenodo.18…
  datacite Molina-Burgos, Francisco 2025 置信度 0.66 GENESISNEURONXPPAUTAUTOCOPASI
• Energy-Efficient Visual Search by Eye Movements with a Low-Latency Spiking Neural Agent
  Human vision incorporates a non-uniform resolution retina, efficient eye movement strategies, and spiking neural networks (SNNs) to balance requirements in visual field size, visual resolution, energy cost, and inference latency. However, whether these feature…
  datacite Yunhui Zhou, Dongqi Han, Yuguo Yu 2025 置信度 0.66 Computer science and technologyArtificial intelligencespiking neural networkeye movementvision
• quantum_inspired_spiking_simulator.py — Quantum-Noise-Injected LIF Neural Simulator
  quantum_inspired_spiking_simulator.py v1.0 — Quantum-Noise-Injected LIF Neural Simulator A zero-setup computational model injecting physically accurate quantum sensor noise (NV-center 1/f or OPM white) into the driving current of a Leaky Integrate-and-Fire (LI…
  datacite B, Britt 2025 置信度 0.66 quantum neuroscienceneurosciencespiking neural networkLIF neuronquantum noise injection
• quantum_inspired_spiking_simulator.py — Quantum-Noise-Injected LIF Neural Simulator
  quantum_inspired_spiking_simulator.py v1.0 — Quantum-Noise-Injected LIF Neural Simulator A zero-setup computational model injecting physically accurate quantum sensor noise (NV-center 1/f or OPM white) into the driving current of a Leaky Integrate-and-Fire (LI…
  datacite B, Britt 2025 置信度 0.66 quantum neuroscienceneurosciencespiking neural networkLIF neuronquantum noise injection
• Signal-SGN++: Topology-Enhanced Time-Frequency Spiking Graph Network for Skeleton-Based Action Recognition
  Graph Convolutional Networks (GCNs) demonstrate strong capability in modeling skeletal topology for action recognition, yet their dense floating-point computations incur high energy costs. Spiking Neural Networks (SNNs), characterized by event-driven and spars…
  datacite Zheng, Naichuan, Lun, Xiahai, Li, Weiyi, Du, Yuchen 2025 置信度 0.66 Computer Vision and Pattern Recognition (cs.CV)Artificial Intelligence (cs.AI)FOS: Computer and information sciencesFOS: Computer and information sciences
• Metaboplasticity: The Reciprocal Regulation of Neuronal Activity and Cellular Energetics
  Standard Spiking Neural Network (SNN) models typically neglect metabolic constraints, treating neurons as energetically unconstrained components. We bridge this gap by implementing a conductance-based leaky integrate-and-fire (gLIF) microcircuit (N=5,000) in B…
  datacite Öner, Ece, Denktaş, Cenk 2025 置信度 0.66 Neurons and Cognition (q-bio.NC)FOS: Biological sciencesFOS: Biological sciences92C05, 92C10, 92C20, 46N60