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• Recursive Harmonic Intelligence: A Unified Field Theory for Geometric AI Training and Manifold Navigation
  Recursive Harmonic Intelligence: A Unified Field Theory for Geometric AI Training and Manifold Navigation Driven by Dean Kulik December 2025 Executive Summary This research report presents a comprehensive theoretical and architectural framework for reconceptua…
  datacite Kulik, Dean 2025 置信度 0.66
• Yatrogenesis/OldiesRules: OldiesRules v0.1.0 - Initial Release
  🎸 OldiesRules v0.1.0 Revival of Classic Academic Simulators in Rust Supported Simulators | Simulator | Era | Purpose | Status | |-----------|-----|---------|--------| | GENESIS | 1988 | Compartmental neural modeling | ✅ Complete | | NEURON | 1984 | Cable equa…
  datacite Frank 2025 置信度 0.66
• Neuromorphic Hardware Systems for Ultra-Low-Power Computing
  Neuromorphic computing represents a paradigm shift in computational architecture, offering unprecedented energy efficiency through brain-inspired hardware implementations. This paper provides a comprehensive analysis of neuromorphic hardware systems designed f…
  datacite Anantharama H 2025 置信度 0.66
• Neuromorphic Hardware Systems for Ultra-Low-Power Computing
  Neuromorphic computing represents a paradigm shift in computational architecture, offering unprecedented energy efficiency through brain-inspired hardware implementations. This paper provides a comprehensive analysis of neuromorphic hardware systems designed f…
  datacite Anantharama H 2025 置信度 0.66
• ElfCore: A 28nm Neural Processor Enabling Dynamic Structured Sparse Training and Online Self-Supervised Learning with Activity-Dependent Weight Update
  In this paper, we present ElfCore, a 28nm digital spiking neural network processor tailored for event-driven sensory signal processing. ElfCore is the first to efficiently integrate: (1) a local online self-supervised learning engine that enables multi-layer t…
  datacite Su, Zhe, Indiveri, Giacomo 2025 置信度 0.66 Hardware Architecture (cs.AR)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• Energy-Efficient Digital Design: A Comparative Study of Event-Driven and Clock-Driven Spiking Neurons
  This paper presents a comprehensive evaluation of Spiking Neural Network (SNN) neuron models for hardware acceleration by comparing event driven and clock-driven implementations. We begin our investigation in software, rapidly prototyping and testing various S…
  datacite Marostica, Filippo, Carpegna, Alessio, Savino, Alessandro, Di Carlo, Stefano 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
• Network-Optimised Spiking Neural Network (NOS) Scheduling for 6G O-RAN: Spectral Margin and Delay-Tail Control
  This work presents a Network-Optimised Spiking (NOS) delay-aware scheduler for 6G radio access. The scheme couples a bounded two-state kernel to a clique-feasible proportional-fair (PF) grant head: the excitability state acts as a finite-buffer proxy, the reco…
  datacite Bilal, Muhammad, Xu, Xiaolong 2025 置信度 0.66 Networking and Internet Architecture (cs.NI)Information Theory (cs.IT)Machine Learning (cs.LG)FOS: Computer and information sciencesFOS: Computer and information sciences
• SNN-Driven Multimodal Human Action Recognition via Sparse Spatial-Temporal Data Fusion
  Multimodal human action recognition based on RGB and skeleton data fusion, while effective, is constrained by significant limitations such as high computational complexity, excessive memory consumption, and substantial energy demands, particularly when impleme…
  datacite Zheng, Naichuan, Xia, Hailun, Liang, Zeyu, Du, Yuchen 2025 置信度 0.66 Computer Vision and Pattern Recognition (cs.CV)FOS: Computer and information sciencesFOS: Computer and information sciences
• Scalable Dendritic Modeling Advances Expressive and Robust Deep Spiking Neural Networks
  Dendritic computation endows biological neurons with rich nonlinear integration and high representational capacity, yet it is largely missing in existing deep spiking neural networks (SNNs). Although detailed multi-compartment models can capture dendritic comp…
  datacite Huang, Yifan, Fang, Wei, Ma, Zhengyu, Li, Guoqi 等 2024 置信度 0.66 Neural and Evolutionary Computing (cs.NE)FOS: Computer and information sciencesFOS: Computer and information sciences
• Photonic Spiking Graph Neural Network for Energy-Efficient Structured Data Processing
  Photonic computing shows great potential for signal processing and artificial intelligence (AI) acceleration due to its ultra-high speed, low energy consumption, and inherent parallelism. Existing photonic computing research has mainly focused on convolutional…
  datacite Yu, Wanting, Xiang, Shuiying, Guo, Xingxing, Shi, Shangxuan 等 2025 置信度 0.66 Optics (physics.optics)FOS: Physical sciencesFOS: Physical sciences
• Responses to transient perturbation can distinguish intrinsic from latent criticality in spiking neural populations
  The critical brain hypothesis posits that neural circuitry operates near criticality to reap the computational benefits of accessing a wide range of timescales. The theory of critical phenomena generally predicts heavy-tailed (power-law) correlations in space …
  datacite Crosser, Jacob T., Brinkman, Braden A. W. 2025 置信度 0.66 Neurons and Cognition (q-bio.NC)Disordered Systems and Neural Networks (cond-mat.dis-nn)FOS: Biological sciencesFOS: Biological sciencesFOS: Physical sciences
• Exploration of time-warped cortical spike time patterns
  The thesis explores the nature of information representation by patterns of action potentials in the cortex. The thesis combines analysis of sensory-evoked in vivo cortical spiking activity, large scale biophysical cortical network simulations, phenomenologica…
  datacite Isbister, James Bryden 2022 置信度 0.66
• Competitive learning in rate coded and spiking neural networks models with applications to vision and audition
  Competitive learning is a common and successful approach used to train unsupervised rate-based neural network models. We apply such a technique in this thesis and produce a rate-coded neural network model of pitch processing which provides insights into the tr…
  datacite Ahmad, Nasir 2019 置信度 0.66 Computational Neuroscience
• NeuRehab: A Reinforcement Learning and Spiking Neural Network-Based Rehab Automation Framework
  Recent advancements in robotic rehabilitation therapy have provided modular exercise systems for post-stroke muscle recovery with basic control schemes. But these systems struggle to adapt to patients' complex and ever-changing behaviour, and to operate within…
  datacite Kambhampati, Phani Pavan, Gautam, Chainesh, Palaniswamy, Jagan, Rao, Madhav 2025 置信度 0.66 Computational Engineering, Finance, and Science (cs.CE)Systems and Control (eess.SY)FOS: Computer and information sciencesFOS: Computer and information sciencesFOS: Electrical engineering, electronic engineering, information engineering
• A scaling-friendly memristor-based leaky integrate-and-fire circuit in a TSMC 28nm process technology
  Spiking neural networks mimic the way the human brain processes data and excel in efficiency. With the advent of the memristor, foundations are laid for scalable and low-power integrated electronic implementations. Key is the small form factor of the memristor…
  datacite Krystofiak, Lukas 2025 置信度 0.66
• Computational neuroscience of speech recognition
  Physical variability of speech combined with its perceptual constancy make speech recognition a challenging task. The human auditory brain, however, is able to perform speech recognition effortlessly. This thesis aims to understand the precise computational me…
  datacite Higgins, Irina 2015 置信度 0.66
• Tabletop Testbed for Low-Power Metamaterial Field Manipulation: A YIG Based Prototype with Retrocausal Control
  This paper presents a fully buildable tabletop-scale testbed for investigating low-power spacetime eld manipulation using a ceramic-YIG (yttrium iron garnet) metamaterial hull integrated with neuromorphic control architecture. The design combines hexagonal YIG…
  datacite Barker, Christian 2025 置信度 0.66
• Energy Optimization Induces Predictive-coding Properties in a Multicompartment Spiking Neural Network Model
  A bstract Predictive coding is a prominent theoretical framework for understanding the hierarchical sensory processing in the brain, yet how it could be implemented in networks of cortical neurons is still unclear. While most existing works have taken a hand-w…
  europepmc Mingfang(Lucy) Zhang, Sander M. Bohte 2024 置信度 0.80
• Efficient and generalizable cross-patient epileptic seizure detection through a spiking neural network.
  Introduction Epilepsy is a global chronic disease that brings pain and inconvenience to patients, and an electroencephalogram (EEG) is the main analytical tool. For clinical aid that can be applied to any patient, an automatic cross-patient epilepsy seizure de…
  europepmc Zongpeng Zhang, Mingqing Xiao, Taoyun Ji, Yuwu Jiang 等 2023 置信度 0.80
• Memristor-based spiking neural network with online reinforcement learning.
  europepmc 2023 置信度 0.80
• A spiking neural network with continuous local learning for robust online brain machine interface.
  europepmc 2024 置信度 0.80
• Functions of Direct and Indirect Pathways for Action Selection Are Quantitatively Analyzed in A Spiking Neural Network of The Basal Ganglia
  europepmc 2024 置信度 0.80
• Retracted: A Deep Spiking Neural Network Anomaly Detection Method.
  europepmc 2023 置信度 0.80
• Electrocardiography Classification with Leaky Integrate-and-Fire Neurons in an Artificial Neural Network-Inspired Spiking Neural Network Framework.
  europepmc 2024 置信度 0.80
• Fractal Spiking Neural Network Scheme for EEG-Based Emotion Recognition.
  europepmc 2024 置信度 0.80
• A Robust Parkinson’s Disease Detection Model Based on Time-varying Synaptic Efficacy Function in Spiking Neural Network
  europepmc 2024 置信度 0.80
• Emotional brain network decoded by biological spiking neural network.
  europepmc 2023 置信度 0.80
• Tiny dLIF: A Dendritic Spiking Neural Network Enabling a Time-Domain Energy-Efficient Seizure Detection System
  europepmc 2024 置信度 0.80
• Emergence of Orientation Pinwheels in a Self-Evolving Spiking Neural Network: Enhancing Visual Coding Efficiency and Reliability
  europepmc 2024 置信度 0.80
• Personalized spiking neural network models inform learning histories of choice behavior
  europepmc 2023 置信度 0.80
• BiœmuS: A new tool for neurological disorders studies through real-time emulation and hybridization using biomimetic Spiking Neural Network.
  Abstract Characterization and modeling of biological neural networks has emerged as a field driving significant advancements in our understanding of brain function and related pathologies. As of today, pharmacological treatments for neurological disorders rema…
  europepmc Romain Beaubois, Jérémy Cheslet, Tomoya Duenki, Giuseppe De Venuto 等 2024 置信度 0.80
• Information Encoding in Bursting Spiking Neural Network Modulated by Astrocytes.
  europepmc 2023 置信度 0.80
• Formation and Retrieval of Cell Assemblies in a Biologically Realistic Spiking Neural Network Model of Area CA3 in the Mouse Hippocampus
  europepmc 2024 置信度 0.80
• Robust compression and detection of epileptiform patterns in ECoG using a real-time spiking neural network hardware framework.
  europepmc 2024 置信度 0.80
• Compact Physical Implementation of Spiking Neural Network Using Ambipolar WSe₂ n-Type/p-Type Ferroelectric Field-Effect Transistor.
  europepmc Jiali Huo, Lingqi Li, Haofei Zheng, Jing Gao 等 2024 置信度 0.80
• EdgeMap: An Optimized Mapping Toolchain for Spiking Neural Network in Edge Computing.
  europepmc 2023 置信度 0.80
• Hippocampome.org 2.0 is a knowledge base enabling data-driven spiking neural network simulations of rodent hippocampal circuits.
  Hippocampome.org is a mature open-access knowledge base of the rodent hippocampal formation focusing on neuron types and their properties. Previously, Hippocampome.org v1.0 established a foundational classification system identifying 122 hippocampal neuron typ…
  europepmc Diek W Wheeler, Jeffrey D Kopsick, Nate Sutton, Carolina Tecuatl 等 2023-09-27T11:32:18Z 置信度 0.80
• Feasibility study on the application of a spiking neural network in myoelectric control systems.
  europepmc 2023 置信度 0.80
• Overview of Spiking Neural Network Learning Approaches and Their Computational Complexities.
  europepmc 2023 置信度 0.80
• Robust compression and detection of epileptiform patterns in ECoG using a real-time spiking neural network hardware framework
  Abstract Interictal Epileptiform Discharges (IED) and High Frequency Oscillations (HFO) in intraoperative electrocorticography (ECoG) may guide the surgeon by delineating the epileptogenic zone. We designed a modular spiking neural network (SNN) in a mixed-sig…
  europepmc Filippo Costa, Eline Schaft, Geertjan Huiskamp, Erik Aarnoutse 等 2023 置信度 0.80
• A Spiking Neural Network with Continuous Local Learning for Robust Online Brain Machine Interface
  europepmc 2023 置信度 0.80
• Spiking neural network with working memory can integrate and rectify spatiotemporal features.
  europepmc 2023 置信度 0.80
• Heterogeneous recurrent spiking neural network for spatio-temporal classification.
  europepmc 2023 置信度 0.80
• Metamodelling of a two-population spiking neural network
  europepmc 2022 置信度 0.80
• Skin Cancer Classification Using Deep Spiking Neural Network.
  europepmc 2023 置信度 0.80
• Artificial cerebellum on FPGA: realistic real-time cerebellar spiking neural network model capable of real-world adaptive motor control.
  europepmc 2024 置信度 0.80
• Biologically Inspired Tonic and Bursting LIF Neuron Model for Spiking Neural Network: A CMOS Implementation
  europepmc 2023 置信度 0.80
• Anti-Disturbance of Scale-Free Spiking Neural Network against Impulse Noise.
  europepmc 2023 置信度 0.80
• Speed-up coherent Ising machine with a spiking neural network.
  europepmc 2023 置信度 0.80
• Extraction of Significant Features by Fixed-Weight Layer of Processing Elements for the Development of an Effective Spiking Neural Network Classifier
  europepmc 2023 置信度 0.80
• A spiking neural network model of cortical intraregional metastability
  europepmc 2022 置信度 0.80
• Design of an experimental setup for delivering intracortical microstimulation in vivo via Spiking Neural Network.
  europepmc 2023 置信度 0.80
• A Deep Spiking Neural Network Anomaly Detection Method.
  europepmc 2022 置信度 0.80
• A Novel Approach for Autonomous Mobile Robot Learning and Control Using a Customized Spiking Neural Network
  europepmc 2023 置信度 0.80
• ALBSNN: ultra-low latency adaptive local binary spiking neural network with accuracy loss estimator.
  europepmc 2023 置信度 0.80
• Construction and improvement of English vocabulary learning model integrating spiking neural network and convolutional long short-term memory algorithm.
  europepmc 2024 置信度 0.80
• MorphBungee: A 65-nm 7.2-mm<sup>2</sup> 27-µJ/Image Digital Edge Neuromorphic Chip With on-Chip 802-Frame/s Multi-Layer Spiking Neural Network Learning.
  europepmc 2025 置信度 0.80
• An unsupervised STDP-based spiking neural network inspired by biologically plausible learning rules and connections.
  europepmc Yiting Dong, Dongcheng Zhao, Yang Li, Yi Zeng 2023 置信度 0.80
• ReplaceNet: real-time replacement of a biological neural circuit with a hardware-assisted spiking neural network.
  europepmc 2023 置信度 0.80
• The NEF-SPA Approach as a Framework for Developing a Neurobiologically Inspired Spiking Neural Network Model for Speech Production.
  Background: The computer-based simulation of the whole processing route for speech production and speech perception in a neurobiologically inspired way remains a challenge. Only a few neural based models of speech production exist, and these models either conc…
  europepmc Bernd J. Kröger 2023 置信度 0.80
• A brain-inspired robot pain model based on a spiking neural network.
  europepmc 2022 置信度 0.80
• Semantic Dynamic Grounding Engine (SDGE)
  SDGE (Semantic Dynamic Grounding Engine) is a dynamic semantic grounding engine built on a spiking neural network (SNN). It treats meaning and understanding as a physical state inside the network, rather than as probabilistic computation over symbols. The Core…
  datacite Salhab, Najih 2026 置信度 0.66 Semantic Dynamic Grounding Engine (SDGE), dynamic semantic grounding, spiking neural networks (SNN), recurrent spiking neural networks (RSNN), attractor dynamics, state-space trajectories, commitment time (t*), committed state (h*), calibrated confidence (sigma_hat*), stability gates, plateau detection, hysteresis, alive gate, event-driven computation, early stopping, energy-efficient AI, few-shot learning, one-shot learning, continual learning, stability–plasticity dilemma, catastrophic forgetting, append-only memory, explicit binding, GroundingBank, temporal drift robustness, Shift300, calibration, expected calibration error (ECE), operational certificates, reproducible artifacts, dynamical systems, brain-inspired computing, neuro-symbolic grounding
• Semantic Dynamic Grounding Engine (SDGE)
  SDGE (Semantic Dynamic Grounding Engine) is a dynamic semantic grounding engine built on a spiking neural network (SNN). It treats meaning and understanding as a physical state inside the network, rather than as probabilistic computation over symbols. The Core…
  datacite Salhab, Najih 2026 置信度 0.66 Semantic Dynamic Grounding Engine (SDGE), dynamic semantic grounding, spiking neural networks (SNN), recurrent spiking neural networks (RSNN), attractor dynamics, state-space trajectories, commitment time (t*), committed state (h*), calibrated confidence (sigma_hat*), stability gates, plateau detection, hysteresis, alive gate, event-driven computation, early stopping, energy-efficient AI, few-shot learning, one-shot learning, continual learning, stability–plasticity dilemma, catastrophic forgetting, append-only memory, explicit binding, GroundingBank, temporal drift robustness, Shift300, calibration, expected calibration error (ECE), operational certificates, reproducible artifacts, dynamical systems, brain-inspired computing, neuro-symbolic grounding
• A bio-inspired hardware implementation of an analog spike-based hippocampus memory model
  The need for processing at the edge the increasing amount of data that is being produced by multitudes of sensors has led to the demand for mode power efficient computational systems, by exploring alternative computing paradigms and technologies. Neuromorphic …
  datacite Casanueva-Morato, Daniel, Ayuso-Martinez, Alvaro, Indiveri, Giacomo, Dominguez-Morales, J.P. 等 2024 置信度 0.66 Hippocampus modelAnalog memory modelSpiking neural networkNeuromorphic engineeringDYNAP-SE
• Prosthetic Hand Manipulation System Based on EMG and Eye Tracking Powered by the Neuromorphic Processor AltAi
  This paper presents a novel neuromorphic control architecture for upper-limb prostheses that combines surface electromyography (sEMG) with gaze-guided computer vision. The system uses a spiking neural network deployed on the neuromorphic processor AltAi to cla…
  datacite Akinshin, Roman, Lopatina, Elizaveta, Bogatikov, Kirill, Kiz, Nikolai 等 2026 置信度 0.66 Robotics (cs.RO)FOS: Computer and information sciencesFOS: Computer and information sciences
• Adaptive transitions in FitzHugh-Nagumo networks with Hebb-Oja coupling rules
  Adaptive coupling in networks of interacting neurons has gained recent attention due to the many applications both in biological and in artificial neural networks, where adaptive coupling or synaptic plasticity is considered as a key factor in learning process…
  datacite Provata, Astero, Boulougouris, George C., Hizanidis, Johanne 2026 置信度 0.66 Pattern Formation and Solitons (nlin.PS)Adaptation and Self-Organizing Systems (nlin.AO)Chaotic Dynamics (nlin.CD)FOS: Physical sciencesFOS: Physical sciences
• SCPN Fusion Core v3.9.0 — Neuromorphic SNN Tokamak Plasma Control Benchmark Suite
  SCPN Fusion Core v3.9.0 — an open-source Python/Rust benchmark suite for tokamak plasma control, featuring the first application of spiking neural networks (SNNs) to magnetic confinement fusion. Key results (100-episode stress-test campaign): - Nengo-SNN (LIF …
  datacite Šotek, Miroslav 2026 置信度 0.66 tokamakplasma controlspiking neural networksneuromorphic computingfusion energy
• SCPN Fusion Core v3.9.0 — Neuromorphic SNN Tokamak Plasma Control Benchmark Suite
  SCPN Fusion Core v3.9.0 — an open-source Python/Rust benchmark suite for tokamak plasma control, featuring the first application of spiking neural networks (SNNs) to magnetic confinement fusion. Key results (100-episode stress-test campaign): - Nengo-SNN (LIF …
  datacite Šotek, Miroslav 2026 置信度 0.66 tokamakplasma controlspiking neural networksneuromorphic computingfusion energy
• 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, Galindo, Pedro L, Donati, Elisa, Indiveri, Giacomo 等 2025 置信度 0.66 570 Life sciences; biology
• Hybrid Spiking Neural Networks: Combining Spike Counts and Membrane Potentials for Energy-Efficient Language and Image Generation
  I propose a hybrid spiking neural network that combines spike counts and membrane potentials for output prediction, extended to both language modeling and image generation tasks. Key Findings (v4 NEW - Image Generation):- Spiking VAE with 50% membrane weight: …
  datacite Funasaki, Hiroto 2026 置信度 0.66 Spiking Neural NetworkLanguage ModelNeuromorphic ComputingEnergy EfficiencyNoise Robustness
• Hybrid Spiking Neural Networks: Combining Spike Counts and Membrane Potentials for Energy-Efficient Language and Image Generation
  I propose a hybrid spiking neural network that combines spike counts and membrane potentials for output prediction, extended to both language modeling and image generation tasks. Key Findings (v4 NEW - Image Generation):- Spiking VAE with 50% membrane weight: …
  datacite Funasaki, Hiroto 2026 置信度 0.66 Spiking Neural NetworkLanguage ModelNeuromorphic ComputingEnergy EfficiencyNoise Robustness
• SNN-Comprypto: High-Performance Compression and Encryption Using Spiking Neural Network Chaotic Reservoir Dynamics
  I propose SNN-Comprypto, a novel system that leverages the chaotic dynamics of Spiking Neural Networks (SNNs) to perform simultaneous high-performance data compression and encryption. Unlike conventional methods that treat compression and encryption as separat…
  datacite Funasaki, Hiroto 2026 置信度 0.66 Spiking Neural NetworksCryptographyReservoir ComputingData CompressionLossless Compression
• SNN-Comprypto: High-Performance Compression and Encryption Using Spiking Neural Network Chaotic Reservoir Dynamics
  I propose SNN-Comprypto, a novel system that leverages the chaotic dynamics of Spiking Neural Networks (SNNs) to perform simultaneous high-performance data compression and encryption. Unlike conventional methods that treat compression and encryption as separat…
  datacite Funasaki, Hiroto 2026 置信度 0.66 Spiking Neural NetworksCryptographyReservoir ComputingData CompressionLossless Compression
• AGI Lux Ferox Project
  This deposit presents the theoretical foundations, mathematical formalism, and practical implementation of the Lux Ferox project — an open research initiative proposing a cognitive architecture grounded in non-equilibrium thermodynamics and information physics…
  datacite MATHIEU, François 2026 置信度 0.66 Artificial intelligenceArtificial Intelligence
• SNN-Genesis v8: AI Comparative Physiology — Universal Homeostatic Set-Point σ≈0.07 Across Three Transformer Architectures
  SNN-Genesis v8 presents a comprehensive framework for LLM safety training using biologically-inspired Spiking Neural Network (SNN) perturbations controlled by Closed-form Continuous-time (CfC) neural networks. The central discovery of v8 is Universal Homeostas…
  datacite Funasaki, Hiroto 2026 置信度 0.66 Spiking Neural NetworksLarge Language ModelsAI SafetyClosed-form Continuous-timeLiquid Neural Networks
• Continual familiarity decoding from recurrent connections in spiking networks
  Familiarity memory enables recognition of previously encountered inputs as familiar without recalling detailed stimuli information, which supports adaptive behavior across various timescales. We present a spiking neural network model with lateral connectivity …
  datacite Zemliak, Viktoria, Pipa, Gordon, Nieters, Pascal 2025 置信度 0.66 Familiarity memorySpiking neural networksSpike-timing-dependent plasticity (STDP)004 - Informatik
• AGI Lux Ferox Project
  This deposit presents the theoretical foundations, mathematical formalism, and practical implementation of the Lux Ferox project — an open research initiative proposing a cognitive architecture grounded in non-equilibrium thermodynamics and information physics…
  datacite MATHIEU, François 2026 置信度 0.66 Artificial intelligenceArtificial Intelligence
• Temporally-Varying Stimulations for Cortical Visual Neuroprosthetic using Spiking Neural Networks
  Visual neuroprosthesis can help to restore a rudimentary form of sight in visually impaired subjects via electrical stimulation. These systems receive camera input images processed by a artificial neural network (ANN) to output stimulation patterns for driving…
  datacite Moure, Pehuen, Pak, Tatyana, Hahn, Niklas, de Ruyter van Stevenick, J 等 2025 置信度 0.66
• Semantic Dynamic Grounding Engine (SDGE): A Mechanical, Certificate-Backed Solution to the Stability–Plasticity Dilemma in Spiking Neural Networks
  SDGE (Semantic Dynamic Grounding Engine) — Description & Significance The Semantic Dynamic Grounding Engine (SDGE) is a software-simulated Spiking Neural Network (SNN) system that frames learning and meaning as a dynamical commitment event rather than a purely…
  datacite Salhab, Najih 2026 置信度 0.66 semantic grounding, spiking neural networks (SNN), continual learning, stability–plasticity dilemma, catastrophic forgetting, N+1 induction, one-shot learning, explicit memory binding, temporal drift robustness, verifiability & reproducibilityArtificial IntelligenceMachine LearningComputational Neuroscience
• Semantic Dynamic Grounding Engine (SDGE): A Mechanical, Certificate-Backed Solution to the Stability–Plasticity Dilemma in Spiking Neural Networks
  SDGE (Semantic Dynamic Grounding Engine) — Description & Significance The Semantic Dynamic Grounding Engine (SDGE) is a software-simulated Spiking Neural Network (SNN) system that frames learning and meaning as a dynamical commitment event rather than a purely…
  datacite Salhab, Najih 2026 置信度 0.66 semantic grounding, spiking neural networks (SNN), continual learning, stability–plasticity dilemma, catastrophic forgetting, N+1 induction, one-shot learning, explicit memory binding, temporal drift robustness, verifiability & reproducibilityArtificial IntelligenceMachine LearningComputational Neuroscience
• The LEGACY Program: AGI Lux Ferox Project
  This deposit presents the theoretical foundations, mathematical formalism, and practical implementation of the Lux Ferox project — an open research initiative proposing a cognitive architecture grounded in non-equilibrium thermodynamics and information physics…
  datacite Lux Ferox Research Collective, Mathieu, François 2026 置信度 0.66 Semantic webQuantum physicsTopologyComputational topologyLogic
• Additional file 1 of A hybrid spiking neural network - quantum framework for spatio-temporal data classification: a case study on EEG data
  (PDF 205 kB)
  datacite Jha, Ravi Kumar, Kasabov, Nikola, Bhattacharyya, Saugat, Coyle, Damien 等 2025 置信度 0.66 StatisticsFOS: MathematicsArtificial Intelligence and Image ProcessingFOS: Computer and information sciences
• Additional file 1 of A hybrid spiking neural network - quantum framework for spatio-temporal data classification: a case study on EEG data
  (PDF 205 kB)
  datacite Jha, Ravi Kumar, Kasabov, Nikola, Bhattacharyya, Saugat, Coyle, Damien 等 2025 置信度 0.66 StatisticsFOS: MathematicsArtificial Intelligence and Image ProcessingFOS: Computer and information sciences
• Sovereign-SNN
  Sovereign-SNN is a vertical, open-source neuromorphic architecture designed to bridge decentralized data streams with physical silicon. This project introduces a "Zero-Permission" methodology for spiking neural network (SNN) research, utilizing standard consum…
  datacite Montoya Cardenas, Raul 2026 置信度 0.66 RustSNNNeuromorphic ComputingDynexRTX 5080
• Sovereign-SNN
  Sovereign-SNN is a vertical, open-source neuromorphic architecture designed to bridge decentralized data streams with physical silicon. This project introduces a "Zero-Permission" methodology for spiking neural network (SNN) research, utilizing standard consum…
  datacite Montoya Cardenas, Raul 2026 置信度 0.66 RustSNNNeuromorphic ComputingDynexRTX 5080
• Energy-Efficient Neuromorphic Hardware. Design and Optimization of Brain-Inspired Computing Paradigms for Spiking Neural Networks
  L'abstract è presente nell'allegato / the abstract is in the attachment
  datacite CARPEGNA, ALESSIO 2025 置信度 0.66 Neuromorphic; Spiking Neural Networks; LIF; FPGA; Neuromorphic accelerators; Edge computing; Artificial Intelligence; Frugal AI; Electronic Design Automation; High-level synthesis; Design Space Exploration; Network Architecture Search; Hyperparameters Optimization; Continual Learning; Latent Replay; Edge Computing; Time Compression; Heart rate; Wrist; Biomedical monitoring ; Wearable devices ; DementiaNeuromorphicSpiking Neural NetworksLIFFPGA
• A model of path integration that connects neural and symbolic representation
  Path integration, the ability to maintain an estimate of one's location by continuously integrating self-motion cues, is a vital component of the brain's navigation system. We present a spiking neural network model of path integration derived from a starting a…
  datacite Cognitive Science Society 2022, Sandra-Yaffa Dumont, Nicole 2022 置信度 0.66 Cognitive ModelingDecision MakingPattern Recognition
• 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 Hochschulschrift
• NeuroIDS: Energy-Efficient Intrusion Detection Using Spiking Neural Networks for Tactical Network Defense
  datacite Davis, Toby 2026 置信度 0.66
• Data supporting: An Energy-Efficient Neuromorphic Front-End for Risk Pre-Screening Using Pulse-Encoded Biosensor Signals
  “An Energy-Efficient Neuromorphic Front-End for Risk Pre-Screening Using Pulse-Encoded Biosensor Signals”, IEEE Sensors Journal, 2024. The dataset includes: Synthetic pulse streams corresponding to free PSA (fPSA) and total PSA (tPSA), generated according to t…
  datacite Chen, Junrui, Pilehvar Meibody, Ali, Carrara, Sandro 2026 置信度 0.66
• Data supporting: An Energy-Efficient Neuromorphic Front-End for Risk Pre-Screening Using Pulse-Encoded Biosensor Signals
  “An Energy-Efficient Neuromorphic Front-End for Risk Pre-Screening Using Pulse-Encoded Biosensor Signals”, IEEE Sensors Journal, 2024. The dataset includes: Synthetic pulse streams corresponding to free PSA (fPSA) and total PSA (tPSA), generated according to t…
  datacite Chen, Junrui, Pilehvar Meibody, Ali, Carrara, Sandro 2026 置信度 0.66
• LIQUID-BRAIN NETWORKS: BIOLOGICALLY INSPIRED FRAMEWORK FOR REAL-TIME ADAPTATION AND LIFELONG LEARNING IN NON-STATIONARY ENVIRONMENTS
  The Liquid-Brain Network (LBN) paradigm draws from biological principles of neural plasticity and real-time adaptation to address challenges posed by non-stationary learning environments. Inspired by neurodynamic plasticity and recurrent attention mechanisms, …
  datacite Researcher 2025 置信度 0.66 Liquid-brain networks, lifelong learning, recurrent attention, non-stationary environments, neural plasticity, biological AI
• LIQUID-BRAIN NETWORKS: BIOLOGICALLY INSPIRED FRAMEWORK FOR REAL-TIME ADAPTATION AND LIFELONG LEARNING IN NON-STATIONARY ENVIRONMENTS
  The Liquid-Brain Network (LBN) paradigm draws from biological principles of neural plasticity and real-time adaptation to address challenges posed by non-stationary learning environments. Inspired by neurodynamic plasticity and recurrent attention mechanisms, …
  datacite Researcher 2025 置信度 0.66 Liquid-brain networks, lifelong learning, recurrent attention, non-stationary environments, neural plasticity, biological AI
• Neural Dynamics: A collection of educational Python scripts
  🚀 Release v1.0.2 In this release, we added a new script nervos_snn_mnist.py that implements a spiking neural network (SNN) for MNIST pattern recognition using the nervos library. This script provides an end-to-end workflow for configuring experiment parameter…
  datacite Musacchio, Fabrizio 2026 置信度 0.66
• Constructive community race: full-density spiking neural network model drives neuromorphic computing
  The local circuitry of the mammalian brain is a focus of the search for generic computational principles because it is largely conserved across species and modalities. In 2014 a model was proposed representing all neurons and synapses of the stereotypical cort…
  datacite Senk, Johanna, Kurth, Anno C., Furber, Steve, Gemmeke, Tobias 等 2025 置信度 0.66 Performance (cs.PF)Distributed, Parallel, and Cluster Computing (cs.DC)FOS: Computer and information sciencesFOS: Computer and information sciences
• A feedback control optimizer for online and hardware-aware training of Spiking Neural Networks
  Unlike traditional artificial neural networks (ANNs), biological neuronal networks solve complex cognitive tasks with sparse neuronal activity, recurrent connections, and local learning rules. These mechanisms serve as design principles in Neuromorphic computi…
  datacite Saponati, Matteo, De Luca, Chiara, Indiveri, Giacomo, Grewe, Benjamin 2026 置信度 0.66 Neural and Evolutionary Computing (cs.NE)Artificial Intelligence (cs.AI)FOS: Computer and information sciencesFOS: Computer and information sciences
• 1234 - Spiking-Aided Neural Architecture for Efficient and Robust WiFi Sensing
  This paper introduces a spiking-aided wifi sensing network (SWS-Net), a novel hybrid neural architecture that integrates Spiking Neural Networks (SNNs) with conventional Artificial Neural Networks (ANNs) for robust WiFi-based indoor sensing. WiFi signals offer…
  datacite Association for Artificial Intelligence 2026, Jing, Liwen, Lu, Yisha, Zhang, Bowen 等 2026 置信度 0.66
• SNN-Genesis: A Preliminary Study on Iterative Adversarial Training of Large Language Models Using Spiking Neural Network Perturbations (v1)
  I present SNN-Genesis, an iterative adversarial training framework for large language models using noise perturbations and a cumulative "Dream Journal" strategy. In each round, noise is injected into the model's hidden layers to elicit adversarial hallucinatio…
  datacite Funasaki, Hiroto 2026 置信度 0.66 Spiking Neural NetworksAdversarial TrainingLarge Language ModelsHallucination MitigationLoRA Fine-tuning
• Neural Dynamics: A collection of educational Python scripts
  🚀 Release v1.0.1 In this release, we added two new scripts stdp_weight_plot.py and stdp_simple_network_example.py to the repository that implement a simple spiking neural network example using spike-timing-dependent plasticity (STDP). These scripts are design…
  datacite Musacchio, Fabrizio 2026 置信度 0.66
• ReLANCE: A Resource-Efficient Low-Latency Cortical Neural Acceleration Engine
  We present a Cortical Neural Pool (CNP) architecture featuring a high-speed, resource-efficient CORDIC based Hodgkin-Huxley (RCHH) neuron model. Unlike shared CORDIC-based DNN approaches, the proposed neuron leverages modular and performance-optimised CORDIC s…
  datacite Kumar, Sonu, Nair, Arjun S., Chaudhary, Bhawna, Lokhande, Mukul 等 2025 置信度 0.66 Neural and Evolutionary Computing (cs.NE)Hardware Architecture (cs.AR)FOS: Computer and information sciencesFOS: Computer and information sciences