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Claude Skills by hiyenwong
github.com/hiyenwong9,934 skills5 installs19,223 views
- Intrinsic Neurosynaptic Spiking MemristiveIntrinsic Neuro-Synaptic Spiking Dynamics in Self-Organizing Memristive Networks (arXiv:2604.18015). Demonstrates that self-assembled memristive networks exhibit intrinsic neuro-synaptic spiking dynamics — including neuronal population bursts, STDP-like learning, emergent oscillatory patterns, multi-modal spike distributions, phase transitions, and criticality signatures — without any external neuron models or transistor-based circuits. Activation: memristive network, spiking dynamics, self-o...Votes: 0GitHub stars: 3
- Knowing When To Stop Delayadaptive Spiking Neural Network Classifiers With Reliability Guarantees**arXiv ID:** 2305.11322 **Authors:** Jiechen Chen, Sangwoo Park, Osvaldo Simeone **Published:** 2023-05-18T22:11:04Z **Abstract:** Spiking neural networks (SNNs) process time-series data via internal event-driven neural dynamics. The energy consumption of an SNN depends on the number of spikes exchanged between neurons over the course of the input presentation. Typically, decisions are produced after the entire input sequence has been processed. This results in latency and energy consumption...Votes: 0GitHub stars: 3
- L Spine Snn Compute EngineL-SPINE 低精度 SIMD 脉冲神经计算引擎方法论。用于资源受限边缘设备的高效 SNN 推理，支持 2/4/8-bit 多精度数据通路，无乘法器 shift-add 模型。适用于神经形态硬件设计、边缘 AI 部署、FPGA SNN 加速。触发词: l-spine, snn hardware, edge inference, low-precision snn, spiking neural compute engineVotes: 0GitHub stars: 3
- Learning Sequence Timing SnnLearning sequence timing and control of replay speed in networks of spiking neurons. The spiking Temporal Memory (sTM) model encodes element-specific timing via sequential neuronal population activation, with oscillatory inputs serving as clock signals for flexible replay speed control.Votes: 0GitHub stars: 3
- Mar Efficient Large Language Models Via Moduleaware Architecture Refinement**arXiv ID:** 2601.21503 **Authors:** Junhong Cai, Guiqin Wang, Kejie Zhao, Jianxiong Tang, Xiang Wang, Luziwei Leng, Ran Cheng, Yuxin Ma, Qinghai Guo **Published:** 2026-01-29T10:21:28Z **Abstract:** Large Language Models (LLMs) excel across diverse domains but suffer from high energy costs due to quadratic attention and dense Feed-Forward Network (FFN) operations. To address these issues, we propose Module-aware Architecture Refinement (MAR), a two-stage framework that integrates State Spac...Votes: 0GitHub stars: 3
- Memristive Neuron Multiple SpikingMultiple spiking functionalities (TTFS, spike count, firing rate) in annealing-optimized Ag/HZO-based memristive neurons. Enables diverse SNN encoding schemes on single hardware device. Updated 2026-04-19 with latest arXiv paper.Votes: 0GitHub stars: 3
- Memristive Spiking Dynamics ResonanceModeling intrinsic neuro-synaptic spiking dynamics and resonance in self-organizing memristive networks. Demonstrates how physical circuits naturally generate neuronal population dynamics similar to biological systems.Votes: 0GitHub stars: 3
- Memristor Preprocessing Reservoir Computing忆阻器预处理储备池计算图像分类方法论。利用忆阻器交叉阵列作为可重构非线性预处理层，结合单层感知机实现高效图像分类。MNIST准确率95.89%，20%器件变异下保持94.2%。忆阻器I-V非线性和记忆效应天然适用于特征变换。Votes: 0GitHub stars: 3
- Memristor Preprocessing ReservoirMemristor-based preprocessing for reservoir computing in image classification. Uses memristor crossbar arrays as reconfigurable nonlinear preprocessing layers to improve accuracy while reducing readout complexity. Keywords: reservoir computing, memristor preprocessing, image classification, crossbar array, nonlinear preprocessing.Votes: 0GitHub stars: 3
- Mixed Signal Feedback Control Snn On ChipMixed-signal hardware implementation of feedback-control optimizer for on-chip SNN training. Combines analog neuromorphic circuits with digital control loops for energy-efficient single-layer spiking neural network learning. Activation: on-chip learning, neuromorphic hardware, mixed-signal SNN, feedback control optimizer, hardware training.Votes: 0GitHub stars: 3
- Modalitydependent Memory Mechanisms In Crossmodal Neuromorphic Computing**arXiv ID:** 2512.18575 **Authors:** Effiong Blessing, Chiung-Yi Tseng, Somshubhra Roy, Junaid Rehman, Isaac Nkrumah **Published:** 2025-12-21T03:18:42Z **Abstract:** Memory-augmented spiking neural networks (SNNs) promise energy-efficient neuromorphic computing, yet their generalization across sensory modalities remains unexplored. We present the first comprehensive cross-modal ablation study of memory mechanisms in SNNs, evaluating Hopfield networks, Hierarchical Gated Recurrent Networks (...Votes: 0GitHub stars: 3
- Morphsnn Adaptive Graph DiffusionMorphSNN: Adaptive graph diffusion and structural plasticity for spiking neural networks. Graph-based SNN with dynamic structure. Activation: morphsnn, graph diffusion, structural plasticity, adaptive snn, graph neural network spiking.Votes: 0GitHub stars: 3
- Morphsnn Structural PlasticityMorphSNN methodology - adaptive graph diffusion and structural plasticity for Spiking Neural Networks. Solves the mismatch between neuron-level dynamics and network-level static connectivity.Votes: 0GitHub stars: 3
- Multi Objective Snn OscillationMulti-objective genetic algorithm (NSGA-III) optimization of Izhikevich neuron-based recurrent spiking neural networks for simultaneously fitting neural firing rates and network oscillation frequencies. Applicable to SNN parameter tuning, brain organoid modeling, and spiking neural network design.Votes: 0GitHub stars: 3
- Multi Objective Snn OscillationMulti-objective genetic algorithm (NSGA-III) optimisation of recurrent spiking neural networks (RSNNs) to match observed neural firing rates and oscillation frequencies simultaneously, including brain organoid data.Votes: 0GitHub stars: 3
- Multi Timescale Conductance Spiking NetworksMulti-Timescale Conductance (MTC) Spiking Networks methodology — sparse, gradient-trainable framework with rich firing dynamics. Derives differentiable conductance-based neurons with fast/slow/ultra-slow timescales, enabling direct BPTT without surrogate gradients. Benchmark: Mackey-Glass chaotic time series forecasting. Activation: MTC-SNN, conductance spiking, BPTT spiking, multi-timescale neuron, Mackey-Glass forecasting, I-V curve shaping, 多时间尺度电导脉冲网络.Votes: 0GitHub stars: 3
- Neko A Library For Exploring Neuromorphic Learning Rules**arXiv ID:** 2105.00324 **Authors:** Zixuan Zhao, Nathan Wycoff, Neil Getty, Rick Stevens, Fangfang Xia **Published:** 2021-05-01T18:50:32Z **Abstract:** The field of neuromorphic computing is in a period of active exploration. While many tools have been developed to simulate neuronal dynamics or convert deep networks to spiking models, general software libraries for learning rules remain underexplored. This is partly due to the diverse, challenging nature of efforts to design new learning r...Votes: 0GitHub stars: 3
- Neuromorphic Aer Encoder DesignUltra-low-power synthesizable asynchronous AER encoder design for neuromorphic edge devices. Tree-based architecture with bundled-data protocol and cross-coupled NAND random-priority arbiter for event collision resolution. Activation: neuromorphic encoder, AER design, asynchronous circuit, spiking neural network hardware, edge device neuromorphic, low-power SNN.Votes: 0GitHub stars: 3
- Neuromorphic Codesign As A Game**arXiv ID:** 2312.14954 **Authors:** Craig M. Vineyard, William M. Severa, James B. Aimone **Published:** 2023-12-11T16:44:02Z **Abstract:** Co-design is a prominent topic presently in computing, speaking to the mutual benefit of coordinating design choices of several layers in the technology stack. For example, this may be designing algorithms which can most efficiently take advantage of the acceleration properties of a given architecture, while simultaneously designing the hardware to supp...Votes: 0GitHub stars: 3
- Neuromorphic Dreaming A Pathway To Efficient Learning In Artificial Agents**arXiv ID:** 2405.15616 **Authors:** Ingo Blakowski, Dmitrii Zendrikov, Cristiano Capone, Giacomo Indiveri **Published:** 2024-05-24T15:03:56Z **Abstract:** Achieving energy efficiency in learning is a key challenge for artificial intelligence (AI) computing platforms. Biological systems demonstrate remarkable abilities to learn complex skills quickly and efficiently. Inspired by this, we present a hardware implementation of model-based reinforcement learning (MBRL) using spiking neural netw...Votes: 0GitHub stars: 3
- Neuromorphic Low Power AiNeuromorphic computing approaches for energy-efficient AI using novel device modalities, compute-in-memory (CIM), analog dynamics, and sparse communication inspired by the brain. Co-design framework spanning materials, circuits, architectures, and algorithms. Activation: neuromorphic computing, low-power AI, compute-in-memory, brain-inspired computing, energy-efficient AI.Votes: 0GitHub stars: 3
- Neuromorphic Parameter Estimation Power ConverterNeuromorphic parameter estimation using Spiking Neural Networks for power converter health monitoring on edge devices. SNN-based real-time parameter estimation for DC-DC converters deployed on neuromorphic hardware. Based on arXiv:2604.15714. Use when: power converter health, edge SNN, neuromorphic hardware, DC-DC converter, real-time health monitoring, energy harvesting, embedded systems.Votes: 0GitHub stars: 3
- Neuromorphic Parametric Oscillators V2Advanced neuromorphic computing based on parametrically-driven oscillators and frequency combs. Uses 2:1 parametric resonance to enable linear and nonlinear memory kernels simultaneously, outperforming conventional delay-line reservoirs on Mackey-Glass prediction and nonlinear channel equalization tasks. Hardware implementation with coupled mechanical oscillators and Duffing oscillator simulations. Activation: neuromorphic computing, parametric resonance, reservoir computing, Duffing oscillat...Votes: 0GitHub stars: 3
- Neuromorphic Parametric OscillatorsNeuromorphic computing using parametrically-driven oscillators and optical frequency combs. Leverages nonlinear parametric resonance in coupled oscillator networks for energy-efficient neural network inference. Keywords: neuromorphic computing, parametric oscillators, frequency combs, optical computing, neural network inference.Votes: 0GitHub stars: 3
- Neuromorphic Photonic NeuronselNeuron Surface Emitting Laser (NeuronSEL) — 基于多结VCSEL的神经形态光子脉冲神经元。利用负微分电阻(NDR)实现类神经元的脉冲发放、不应期和整合-发放动力学。适用于光子计算、光通信、神经形态硬件、光学传感。Activation: neuromorphic photonics, VCSEL spiking neuron, negative differential resistance, optical neural network, photonic computing, integrate-and-fire laserVotes: 0GitHub stars: 3
- Neuromorphic Spiking Ring AttractorNeuromorphic Spiking Ring Attractor for proprioceptive joint-state estimation. Implements neural clusters with local recurrent excitation and global inhibition to encode joint angles on neuromorphic processors like DYNAP-SE2. Activation: spiking ring attractor, proprioceptive encoding, neuromorphic joint estimation, SRA network.Votes: 0GitHub stars: 3
- Neuromorphic Supremacy Hybrid Astrocytic SpikingThe Neuromorphic Supremacy paradigm demonstrates that embedding genuine neuromorphic circuits (astrocytic modulation + spiking dynamics) into conventional ANN architectures enables superior performance in data-scarce and noisy environments — a regime where classical deep learning collapses.Votes: 0GitHub stars: 3
- Neuronas Enhancing Efficiency Of Neuromorphic Inmemory Computing For Intelligent Mobile Agents Through Hardwareaware Spiking Neural Architecture Search**arXiv ID:** 2407.00641 **Authors:** Rachmad Vidya Wicaksana Putra, Muhammad Shafique **Published:** 2024-06-30T09:51:58Z **Abstract:** Intelligent mobile agents (e.g., UGVs and UAVs) typically demand low power/energy consumption when solving their machine learning (ML)-based tasks, since they are usually powered by portable batteries with limited capacity. A potential solution is employing neuromorphic computing with Spiking Neural Networks (SNNs), which leverages event-based computation to...Votes: 0GitHub stars: 3
- Neuroscience Spiking Afe 20260714Skill summarizing the 32-channel event-based bio-signal analog front-end with adaptive delta and pulse frequency encoding for neuromorphic signal processing.Votes: 0GitHub stars: 3
- Neurotrain Local Learning Snn BenchmarkingNeuroTrain methodology for surveying and benchmarking local learning rules in Spiking Neural Networks (SNNs). Covers comprehensive taxonomy of SNN training algorithms (surrogate-gradient BP, local/three-factor rules, bio-inspired plasticity, ANN-to-SNN conversion, non-standard optimization) and provides open-source snnTorch-based benchmarking framework. Use when: benchmarking SNN training algorithms, comparing local learning rules, implementing biologically plausible plasticity, or surveying ...Votes: 0GitHub stars: 3
- Numa Balancing Snn PerformanceMethodology for optimizing spiking neural network simulation performance by managing NUMA balancing settings on HPC systems.Votes: 0GitHub stars: 3
- Numa Balancing Spiking Network HpcNUMA balancing performance optimization for spiking network simulations on HPC systems. Identifies that automatic NUMA balancing can reduce energy efficiency by 30% in spiking network simulations and provides methodology for per-job NUMA balancing control to optimize performance and energy consumption.Votes: 0GitHub stars: 3
- On The Robustness Of A Neural Network**arXiv ID:** 1707.08167 **Authors:** El Mahdi El Mhamdi, Rachid Guerraoui, Sebastien Rouault **Published:** 2017-07-25T19:22:55Z **Abstract:** With the development of neural networks based machine learning and their usage in mission critical applications, voices are rising against the \textit{black box} aspect of neural networks as it becomes crucial to understand their limits and capabilities. With the rise of neuromorphic hardware, it is even more critical to understand how a neural networ...Votes: 0GitHub stars: 3
- Oscillatory Snn Time Delayed CoordinationOscillatory Spiking Neural Network with time-delayed coordination methodology. Models cognition-level neural synchrony emerging from iterative bottom-up and top-down interactions between micro-scale spiking dynamics and macro-scale oscillatory synchronization. Use when studying S2-Net, spiking-by-synchronization, oscillatory neural networks, time-delayed coordination, cortical rhythm modeling, temporal binding, or brain-inspired learning primitives.Votes: 0GitHub stars: 3
- Parametric Oscillator NeuromorphicNeuromorphic computing based on parametrically-driven oscillators and frequency combs. Implements reservoir computing using 2:1 parametric resonance. Activation: parametric oscillator, neuromorphic, reservoir computing, frequency comb, nonlinear dynamics.Votes: 0GitHub stars: 3
- Pdds Spiking Insulin DeliveryPDDS event-driven spiking neural network pipeline for personalized insulin delivery — LIF neurons with Poisson encoding on OhioT1DM dataset achieving 85.24% accuracy. Activation: insulin delivery, spiking neural network, healthcare, event-driven, personalized medicine, blood glucose, SNN, neuromorphic.Votes: 0GitHub stars: 3
- Practical Bayesian Inference Speech SnnsPractical Bayesian inference for Spiking Neural Networks with uncertainty quantification and loss-landscape smoothing. Addresses angular/irregular predictive landscapes in spike-based neural computation. Activation: bayesian snn, uncertainty quantification, speech spiking neural network, loss landscape, spiking neural network bayesian inference.Votes: 0GitHub stars: 3
- Practical Bayesian Speech SnnsPractical Bayesian Inference for Spiking Neural Networks in Speech Recognition. Enables uncertainty-aware speech processing with SNNs, combining Bayesian deep learning with spiking dynamics for robust speech recognition under noisy conditions. Activation: Bayesian SNN, speech recognition, uncertainty quantification, variational inference, spiking speech, robust speech, noisy speech, SNN uncertainty.Votes: 0GitHub stars: 3
- Predictive Coding As A Neuromorphic Alternative To Backpropagation A Critical Evaluation**arXiv ID:** 2304.02658 **Authors:** Umais Zahid, Qinghai Guo, Zafeirios Fountas **Published:** 2023-04-05T11:48:47Z **Abstract:** Backpropagation has rapidly become the workhorse credit assignment algorithm for modern deep learning methods. Recently, modified forms of predictive coding (PC), an algorithm with origins in computational neuroscience, have been shown to result in approximately or exactly equal parameter updates to those under backpropagation. Due to this connection, it has been...Votes: 0GitHub stars: 3
- Qslm A Performance And Memoryaware Quantization Framework With Tiered Search Strategy For Spikedriven Language Models**arXiv ID:** 2601.00679 **Authors:** Rachmad Vidya Wicaksana Putra, Pasindu Wickramasinghe, Muhammad Shafique **Published:** 2026-01-02T13:05:33Z **Abstract:** Large Language Models (LLMs) have been emerging as prominent AI models for solving many natural language tasks due to their high performance (e.g., accuracy) and capabilities in generating high-quality responses to the given inputs. However, their large computational cost, huge memory footprints, and high processing power/energy make ...Votes: 0GitHub stars: 3
- Quantized Snn Beyond AccuracyQuantization of Spiking Neural Networks beyond accuracy - Earth Mover's Distance (EMD) methodology for evaluating SNN quantization effects on spike train distributions, firing patterns, and temporal dynamics. Use when optimizing SNNs for low-precision hardware, analyzing quantization trade-offs in neuromorphic systems. Triggers: SNN quantization, spiking network compression, low-precision SNN, Earth Mover's Distance, neuromorphic hardware optimization.Votes: 0GitHub stars: 3
- Replay4ncl An Efficient Memory Replaybased Methodology For Neuromorphic Continual Learning In Embedded Ai Systems**arXiv ID:** 2503.17061 **Authors:** Mishal Fatima Minhas, Rachmad Vidya Wicaksana Putra, Falah Awwad, Osman Hasan, Muhammad Shafique **Published:** 2025-03-21T11:33:22Z **Abstract:** Neuromorphic Continual Learning (NCL) paradigm leverages Spiking Neural Networks (SNNs) to enable continual learning (CL) capabilities for AI systems to adapt to dynamically changing environments. Currently, the state-of-the-art employ a memory replay-based method to maintain the old knowledge. However, this te...Votes: 0GitHub stars: 3
- Residual Membrane Potential Alignment SnnANN-SNN conversion with membrane potential alignment.Votes: 0GitHub stars: 3
- Rhythm Snn Temporal ProcessingNeural oscillation-inspired SNN architecture for enhanced temporal processing and noise robustness. Based on Nature Communications 2025 Rhythm-SNN paper.Votes: 0GitHub stars: 3
- Rippled Graphene Fluidic Memristor NeuromorphicNanofluidic memristive devices using rippled graphene pores for ionic memory, synaptic plasticity emulation, and neuromorphic circuit design. Covers graphene pore engineering, ion-selective memory effects, programmable conductance modification via voltage spikes, and integrated ionic circuits for image identification and neural signal analysis.Votes: 0GitHub stars: 3
- Robust Spiking ReservoirRobust Spiking Reservoir Computing methodology for energy-efficient temporal processing. Implements edge-of-chaos tuning with experimental uncertainty handling, bridging abstract criticality notions with practical reservoir computing applications. Based on Freddi et al. (2026) arXiv:2604.06395v1.Votes: 0GitHub stars: 3
- Scalable Eventbyevent Processing Of Neuromorphic Sensory Signals With Deep Statespace Models**arXiv ID:** 2404.18508 **Authors:** Mark Schöne, Neeraj Mohan Sushma, Jingyue Zhuge, Christian Mayr, Anand Subramoney, David Kappel **Published:** 2024-04-29T08:50:27Z **Abstract:** Event-based sensors are well suited for real-time processing due to their fast response times and encoding of the sensory data as successive temporal differences. These and other valuable properties, such as a high dynamic range, are suppressed when the data is converted to a frame-based format. However, most cu...Votes: 0GitHub stars: 3
- Scalable Multitask Learning Through Spiking Neural Networks With Adaptive Taskswitching Policy For Intelligent Autonomous Agents**arXiv ID:** 2504.13541 **Authors:** Rachmad Vidya Wicaksana Putra, Avaneesh Devkota, Muhammad Shafique **Published:** 2025-04-18T08:12:59Z **Abstract:** Training resource-constrained autonomous agents on multiple tasks simultaneously is crucial for adapting to diverse real-world environments. Recent works employ reinforcement learning (RL) approach, but they still suffer from sub-optimal multi-task performance due to task interference. State-of-the-art works employ Spiking Neural Networks (...Votes: 0GitHub stars: 3
- Scalable Snn Gpu ClustersScalable SNN simulation framework using GPU cluster parallelization. Addresses computational bottlenecks in large-scale spiking neural network simulation through distributed GPU computing, optimized event-driven architectures, and efficient spike communication protocols. Activation: scalable snn, gpu cluster spiking simulation, distributed snn computing, large scale neural simulation, parallel snn, gpu spiking network, distributed neural simulationVotes: 0GitHub stars: 3
- Scaling Up Dynamic Graph Representation Learning Via Spiking Neural Networks**arXiv ID:** 2208.10364 **Authors:** Jintang Li, Zhouxin Yu, Zulun Zhu, Liang Chen, Qi Yu, Zibin Zheng, Sheng Tian, Ruofan Wu, Changhua Meng **Published:** 2022-08-15T09:22:15Z **Abstract:** Recent years have seen a surge in research on dynamic graph representation learning, which aims to model temporal graphs that are dynamic and evolving constantly over time. However, current work typically models graph dynamics with recurrent neural networks (RNNs), making them suffer seriously from compu...Votes: 0GitHub stars: 3