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Claude Skills by hiyenwong
github.com/hiyenwong9,934 skills5 installs19,223 views
- Neural Behavioral Whole Body Movement MonkeysNeural-behavioral representation framework for natural whole-body movement in primates. Combines large-scale epidural cortical signals with synchronized multi-view motion capture to decode unconstrained whole-body kinematics. Use when: (1) decoding natural whole-body movements, (2) modeling neural-behavioral representations, (3) primate motor neuroscience research, (4) developing behavior priors for movement decoding. Keywords: whole-body movement, motor decoding, primate neuroscience, neural...Votes: 0GitHub stars: 3
- Neural Brain FrameworkNeuroscience-inspired framework for embodied AI agents. Use when building embodied agents, designing neural brain architectures, integrating multimodal sensing with cognition, implementing neuroplasticity-based memory systems, or optimizing neuromorphic hardware/software for real-world autonomous systems. Covers active sensing, perception-cognition-action loop, adaptive memory, and energy-efficient neuromorphic design.Votes: 0GitHub stars: 3
- Neural Code Dynamics Analysis神经编码动力学分析框架 - 整合计算神经科学、机器学习和临界态理论，研究生物与人工神经网络编码表示动力学。涵盖临界脑假说、雪崩动力学、信息几何与动力学不变量。Activation: neural coding, dynamics analysis, critical brain hypothesis, avalanche dynamics, information geometry, dynamical invariants, neural representation, encoding dynamics, computational neuroscience.Votes: 0GitHub stars: 3
- Neural Code Language CharacterizationClosed-loop framework for automated neuron characterization using natural language descriptions. Translates neuron activation patterns into semantic hypotheses, verifies them via in silico experiments using neural digital twins. Use when: neuron selectivity analysis, neural code interpretation, automated neuroscience discovery, V1/V4 characterization, digital twin experiments, semantic description of neurons, generative model for neuroscience.Votes: 0GitHub stars: 3
- Neural Code SpeakAutomated characterization of individual neurons through natural language using generative models and neural digital twins. Use when: studying neuron selectivity in visual cortex, building closed-loop frameworks for neural characterization, generating semantic hypotheses for neural tuning, or doing automated neuron description via vision-language models.Votes: 0GitHub stars: 3
- Neural Computation Without SlotsBiologically plausible memory and attention without dedicated storage slots. Extends Modern Hopfield Networks (MHN) to K-winner ensembles for improved continual learning retention, and demonstrates MHN can capture slot-based memory functions of LLMs. Activation: memory without slots, biologically plausible attention, K-winer Hopfield network, ensemble memory MHN, McClelland memory model.Votes: 0GitHub stars: 3
- Neural Connectivity Matrix Viewer脑连接矩阵交互式可视化工具。基于HTML5/JavaScript的浏览器端应用，支持EEG、ECoG、MEG、fMRI等高维神经连接数据的3D堆叠矩阵可视化，实时交互探索连接模式。适用于脑连接分析、神经数据可视化、连接组学。触发词：脑连接可视化、连接矩阵、神经网络可视化、connectivity matrix、brain connectivity visualization、EEG connectivity、MEG connectivity。Votes: 0GitHub stars: 3
- Neural Critical Dynamics TheoryTheory of critical dynamics and information processing in neural networks. Neural systems at critical points exhibit optimal information processing, maximal dynamic range, and power-law distributed avalanches. Provides methods for identifying, analyzing, and exploiting critical regimes in both biological and artificial neural networks. Applicable to critical brain hypothesis, neural avalanche analysis, optimal computation regimes. Trigger: neural criticality, critical dynamics, neural avalanc...Votes: 0GitHub stars: 3
- Neural Decoder Quantum Error CorrectionDeep learning-based decoders for quantum error correction (QEC) that outperform traditional algorithms (MWPM, belief propagation) in speed and adaptability to realistic noise models.Votes: 0GitHub stars: 3
- Neural Digital Twins BciNeural Digital Twins framework for Brain-Computer Interfaces (BCIs). Addresses neuroplasticity-induced recalibration, session-to-session variability, and real-time adaptation through personalized brain models.Votes: 0GitHub stars: 3
- Neural Dynamics Analysis MethodologyComprehensive framework for neural dynamics analysis integrating multiple methodologies: (1) Neural population decoding and encoding, (2) Brain network dynamics modeling, (3) Neural criticality assessment, (4) Spiking neural network dynamics, (5) Brain-connectome computational analysis. Use when studying neural system dynamics, brain network evolution, neural population behavior, or implementing computational neuroscience models.Votes: 0GitHub stars: 3
- Neural Dynamics Decision MakingNeural Dynamics Decision-Making ModelsVotes: 0GitHub stars: 3
- Neural Dynamics Universal Translator FoundationFoundation model for neural spiking data using multi-task masking (MtM) to translate across population, region, and single-neuron levels. Enables zero-shot and few-shot brain decoding across multiple brain areas. Activation triggers: neural translator, foundation model spiking, MtM, multi-task masking, IBL dataset, brain decoding.Votes: 0GitHub stars: 3
- Neural Dynamics Universal Translator神经动力学通用翻译器方法论。在单细胞、单脉冲分辨率下翻译不同神经模型的动力学，实现跨模型动力学对齐。适用于神经元模型转换、动力学分析、计算神经科学。触发词：神经动力学、模型翻译、脉冲分辨率、神经元模型、neural dynamics、universal translator、single-spike。Votes: 0GitHub stars: 3
- Neural Emulator TheoryNeural Emulator TheoryVotes: 0GitHub stars: 3
- Neural Encoding Evaluation Ground TruthSystematic audit methodology for EEG foundation model interpretability. Decomposes what models learn, what they use, and how much can be explained using layer-wise ridge probing, LEACE cross-covariance erasure, and transparent classifiers. Use when: EEG foundation model analysis, neural encoding evaluation, interpretability audit, feature causality analysis, brain signal representation analysis, EEG feature lexicon, LEACE analysis.Votes: 0GitHub stars: 3
- Neural Encoding Evaluation MeegEvaluation framework for neural encoding models using MEEG (Mutual-information-based Estimation of Encoding model goodness-of-fit). Provides systematic methodology for assessing how well neural models predict brain activity, with information-theoretic metrics and cross-validation protocols.Votes: 0GitHub stars: 3
- Neural Eso Robust ControlNeural Extended State Observer (Neural-ESO) dual-pathway architecture for provably robust learning-based control systems. Combines neural network feedforward disturbance estimation with classical ESO corrective pathway, guaranteeing uniform ultimate boundedness via Lyapunov theory and small-gain analysis.Votes: 0GitHub stars: 3
- Neural Fields World ModelsNeural Fields as World Models methodology — isomorphic world models that preserve sensory topology for physics prediction as geometric propagation rather than abstract state transition. Motor-gated neural fields with local lateral connectivity and action-conditional prediction within spatial maps. Use for: world model architectures, sensory cortex modeling, offline task learning, action-conditional prediction, spatial prediction, embodied AI, neural field implementations. Activation: neural f...Votes: 0GitHub stars: 3
- Neural Interface Safety AuditSafety audit framework for neural interface models. Identifies three alignment failures: verification insufficiency (certificates pass while accuracy drops), proxy-fidelity divergence (task optimization damages neural signals), and latent information exfiltration (private attributes leak from embeddings). Activation: neural interface safety, BCI security, EEG robustness, brain-computer interface audit, 神经接口安全, 脑机接口审计Votes: 0GitHub stars: 3
- Neural Inverse Design Scintillator MedicalNeural network inverse design of nanophotonic scintillators for medical imaging. Uses physics-informed neural networks to learn scintillation cascade processes from incident particles to photon emission, enabling end-to-end differentiable optimization of scintillator geometry. Use when designing scintillators for X-ray imaging, medical radiation detectors, or optimizing photon emission patterns via inverse design.Votes: 0GitHub stars: 3
- Neural Lyapunov VerificationSound and complete verification of neural Lyapunov candidates for nonlinear control systems. Uses hyperplane partitioning of ReLU networks to verify stability guarantees. Use when: (1) verifying neural network controller stability, (2) Lyapunov function validation, (3) formal verification of learned control policies, (4) safety-critical neural control systems, (5) analyzing ReLU network dynamics for stability properties.Votes: 0GitHub stars: 3
- Neural Manifold Dynamics LearningNeural Manifold Learning Dynamics methodology for analyzing population activity in high-dimensional neural state spaces. Extracts low-dimensional structure from neural recordings to understand computation and behavior. Combines dimensionality reduction with dynamical systems analysis for neural population decoding. Activation: neural manifold, latent dynamics, population activity, dimensionality reduction, neural state space, behavior decoding, jPCA, dPCA, GPFA.Votes: 0GitHub stars: 3
- Neural Manifold Learning DynamicsNeural manifold learning dynamics methodology for analyzing population activity in high-dimensional neural state spaces. Extracts low-dimensional structure from neural recordings to understand computation and behavior. Activation triggers: neural manifold, latent dynamics, population activity, dimensionality reduction, neural state space, behavior decoding.Votes: 0GitHub stars: 3
- Neural Manifolds Crystallized EmbeddingsNeural manifolds as crystallized embeddings: a synthesis of free energy principle, generalized synchronization, and Hebbian plasticity. Proposes that neural manifolds emerge developmentally through three interacting processes: dynamical contraction (free energy minimization), generalized synchronization (reservoir computing embedding), and correlation-based Hebbian plasticity that crystallizes embedded manifolds into recurrent connectivity. Use when studying neural manifold formation, head-di...Votes: 0GitHub stars: 3
- Neural Mass Models UnifiedUnified Rosetta Stone framework for neural mass models. Provides mathematical tools connecting different neural mass model formulations for brain dynamics analysis across scales from single-neuron spiking to macroscopic fMRI/MEG/EEG. Applies to: brain dynamics modeling, neural mass models, computational neuroscience, multi-scale brain modeling. Activation: neural mass models, rosetta stone neural, brain dynamics tools, neural mass unified, computational brain modeling.Votes: 0GitHub stars: 3
- Neural Network Quantum States Grand CanonicalNeural network quantum state (NQS) architecture for grand canonical ensemble bosonic systems. Enables variational Monte Carlo with variable particle number in Fock space. Activation: neural quantum states, grand canonical ensemble, bosonic wavefunctions, Fock space, variational Monte Carlo, NQS, quantum many-body ground state.Votes: 0GitHub stars: 3
- Neural Ode Mean Field TrainingTheory of learning high-dimensional controlled non-linear dynamical systems via neural ODEs trained with online stochastic gradient descent, solved using dynamical mean field theory. Activation: neural ode, mean field theory, dynamical systems, training dynamics, learning curves, high-dimensional limit, statistical mechanics, online SGD, ResNet theory.Votes: 0GitHub stars: 3
- Neural Operator Adaptive Pde ControlDual-learning architecture combining online adaptive control with offline neural operator approximation for backstepping control of nonlinear hyperbolic PDEs with unknown Volterra series. Use when controlling PDE systems where gain computation is prohibitively expensive for real-time, particularly for fluid dynamics, traffic flow, or heat transfer systems.Votes: 0GitHub stars: 3
- Neural Operator Stability DiscoveryNeural operator framework for data-driven discovery of stability and receptivity properties in physical systems. Activation: neural operator, stability discovery, receptivity analysis, dynamical systems.Votes: 0GitHub stars: 3
- Neural Phase CorrelationLearned generalization of phase correlation that lifts the fixed Fourier basis restriction to discover unknown transformations between observations. Applicable to image registration, non-rigid deformation, and quantum Hamiltonian eigenstate recovery from observation pairs.Votes: 0GitHub stars: 3
- Neural Population DecodingNeural population decoding methods for analyzing high-dimensional neural recordings. Focuses on decoding cognitive states, working memory, and behavior from population activity using dimensionality reduction and dynamical systems approaches.Votes: 0GitHub stars: 3
- Neural Population DynamicsMethods for analyzing neural population dynamics including dimensionality reduction, trajectory analysis, and dynamical systems modeling. Covers techniques for understanding how populations of neurons encode information and generate behavior. Use when analyzing neural population recordings, performing dimensionality reduction on neural data, modeling neural dynamics, or studying neural trajectories.Votes: 0GitHub stars: 3
- Neural Qaoa Differentiable OptimizationNeural QAOA² methodology: end-to-end differentiable framework for joint graph partitioning and QAOA parameter initialization. Uses generative evaluative network (GEN) with differentiable quantum evaluator for gradient-guided learning. Ranks first on 101/183 instances with zero-shot generalization. Activation: neural QAOA, quantum optimization initialization, graph partitioning QAOA, differentiable quantum, QAOA2.Votes: 0GitHub stars: 3
- Neural Qaoa OptimizationNeural QAOA² methodology - using neural networks for differentiable graph partitioning and parameter initialization in quantum combinatorial optimization. Bridges ML and QAOA for scalable NISQ optimization.Votes: 0GitHub stars: 3
- Neural Quantum Graph EmbeddingNeural-enhanced optimization framework for quantum architecture embedding problems using Distance Encoder Networks. Solves constrained unit disk problems for neutral atom qubit positioning via modified autoencoder with custom Embedding Loss Function. Activation: quantum embedding, unit disk problem, neutral atom qubits, distance encoder network, qubit positioning, quantum architecture optimization.Votes: 0GitHub stars: 3
- Neural Quantum Spectral Operator PdeNeural Variational Quantum Linear Solver (NVQLS) - first hybrid quantum-classical operator learning framework using Legendre-Galerkin weak formulation for solving parametric PDEs. Achieves superior accuracy with theoretical computational complexity advantages under efficient state preparation. Activation: quantum operator learning, quantum PDE solver, variational quantum linear solver, VQLS, quantum spectral method, quantum Galerkin method.Votes: 0GitHub stars: 3
- Neural Qubit EmbeddingNeural-powered unit disk graph embedding for mapping QUBO problems onto quantum annealer connectivity. Uses graph neural networks to solve the minor embedding problem efficiently.Votes: 0GitHub stars: 3
- Neural Receptive Fields Hyperbolic GeometryNeural Receptive Fields via Hyperbolic GeometryVotes: 0GitHub stars: 3
- Neural Receptive Fields Scale Free GeometryGeometric framework for neural receptive field emergence in scale-free networks. Studies how receptive fields organize and couple with stimulus space embedding without fine-tuning. Activation: receptive field geometry, scale-free networks, stimulus space embedding, neural geometry.Votes: 0GitHub stars: 3
- Neural Representation Reshaping MechanismsUnified framework synthesizing neural/artificial neural network representation reshaping mechanisms across four paradigms: (1) Embodied VR feedback reshapes motor representations for BCI decoding, (2) fMRI visual question answering decodes reshaped representations, (3) Common noise induces group-level synchronization reshaping oscillator dynamics, (4) LLM in-context learning reorganizes representational geometry. Provides cross-domain principles for representation manipulation, decoding strat...Votes: 0GitHub stars: 3
- Neural Tracking Correlation InterpretationNeural tracking correlation interpretation with null dist.Votes: 0GitHub stars: 3
- Neural Variability Enhances Robustness神经变异性增强人工神经网络鲁棒性方法论。研究相关性噪声如何改善对抗攻击和自然图像修改的鲁棒性，建立生物学可解释的鲁棒神经网络设计策略。Votes: 0GitHub stars: 3
- Neurally Guided Adversarial RobustnessDissociating spatial frequency reliance from adversarial robustness in neurally aligned DCNNs. Shows that adversarial robustness from neural alignment is NOT primarily driven by spatial frequency bias (LSF or human channel), but by deeper representational properties. Use when: analyzing neural alignment robustness, spatial frequency analysis of DCNNs, adversarial attack defense mechanisms, ventral visual stream modeling, or brain-inspired CNN robustness. Activation: neural alignment robustnes...Votes: 0GitHub stars: 3
- Neuralset Neuro Ai FrameworkNeuralSet unified Python framework for Neuro-AI research, harmonizing diverse neural recordings (fMRI, M/EEG, spikes) with deep learning embeddingsVotes: 0GitHub stars: 3
- Neuro Attractor Landscape Working MemoryAttractor landscape methodology for working memory in neural circuits. Analyzes how persistent neural activity patterns form stable attractor states that encode and maintain information during delay periods. Uses dynamical systems theory, bifurcation analysis, and manifold reconstruction to characterize working memory mechanisms.Votes: 0GitHub stars: 3
- Neuro Memory ArchitectureNeuroscience-inspired memory architecture design for AI agents. Maps biological memory systems (working, short-term, episodic, semantic, procedural, core, cross-context) to AI agent memory layers. Integrates neuroscience models including Hebbian learning, synaptic consolidation, sleep-based replay, and active forgetting. Use when designing agent memory systems, building neuro-inspired AI architectures, or implementing biologically-plausible memory mechanisms. Triggers: neuro memory, brain-ins...Votes: 0GitHub stars: 3
- Neuro Quantum ResearchResearch methodology for the intersection of neuroscience and quantum physics/computing. Use when analyzing papers or research at the boundary of brain science and quantum mechanics, including quantum neural networks, quantum memory models of cognition, quantum-inspired brain simulation, quantum computing for neuroscience, and quantum effects in biological systems. Triggers: quantum neuroscience, quantum brain, quantum neural network, quantum memory, quantum cognition, neuromorphic quantum, q...Votes: 0GitHub stars: 3
- Neuro Symbolic Cognitive ArchitecturesNeural-Symbolic Cognitive Architectures combining neural networks with symbolic reasoning for interpretable, robust AI systems. Enables explicit knowledge representation, logical inference, and learning from both data and rules. Applicable to reasoning systems, knowledge-intensive tasks, interpretable AI. Trigger: neuro-symbolic AI, symbolic reasoning neural networks, interpretable reasoning, knowledge representation learning, neural-symbolic integrationVotes: 0GitHub stars: 3
- Neuro Vesicles NeuromodulationNeuro-Vesicles framework for dynamical neuromodulation in neural networks. Introduces mobile discrete vesicle population as event-based interaction layer alongside network tensors. Applies to: neuromodulation, dynamic network modulation, spiking networks, neuromorphic hardware. Activation: neuro vesicles, neuromodulation dynamical, mobile modulation, vesicle framework, programmable neuromodulation.Votes: 0GitHub stars: 3