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Claude Skills by hiyenwong
github.com/hiyenwong9,934 skills5 installs19,223 views
- Phinn Eeg Topological Dream AnalysisTopological time-series analysis methodology for dream-state EEG using Dynamic Betti curves, persistent homology, and topology-conditioned neural signal synthesis. arXiv:2607.09662Votes: 0GitHub stars: 3
- Photonic Deep Quantum Neural NetworkPhotonic-implemented deep quantum neural network via virtual-driven Hilbert space expansion. Enables effective non-unitary and nonlinear activation functions on linear programmable quantum photonic chips using input replication and mode expansion. Use when: implementing QNNs on photonic platforms, quantum neural activation functions, quantum photonic computing, deep quantum networks, non-unitary quantum operations.Votes: 0GitHub stars: 3
- Photonic Quantum Hopfield MemoryPhotonic quantum simulation of associative memory using Hopfield models with multi-body interactions. Use when simulating neural network dynamics, associative memory retrieval, spin-glass phases, or p-body Hopfield Hamiltonians on quantum hardware. Covers photonic quantum processors, Ising-like neurons via binary phase shifters, memory retrieval phases, and experimental observation of associative memory. Triggers: quantum associative memory, photonic Hopfield model, quantum neural network sim...Votes: 0GitHub stars: 3
- Physical Neural Computing ReviewComprehensive review of physical neural computing substrates beyond silicon: memristive devices, photonic circuits, mechanical metamaterials, microfluidic networks, and chemical reaction systems. Use when designing neuromorphic hardware, evaluating physical substrate alternatives, or researching energy-efficient AI deployment at the edge. Triggers: physical neural computing, neuromorphic substrate, memristor neural networks, photonic neural networks, analog AI, edge AI hardware, non-silicon n...Votes: 0GitHub stars: 3
- Physics Aware Spiking HarPAS-Net: Physics-aware SNN for energy-efficient HAR using physics-informed regularization. Activation: physics-aware SNN, human activity recognition, biomechanical constraints.Votes: 0GitHub stars: 3
- Physiologically Constrained Musculoskeletal Neural Network生理约束肌肉骨骼神经网络（MSK-NN）从部分观测sEMG估计多自由度关节运动学，无需内部生物力学标签的直接监督。Votes: 0GitHub stars: 3
- Pinn Neuronal Parameter Estimation使用物理信息神经网络(PINN)进行神经元模型参数估计和状态重构的方法论。仅需要部分电压观测即可重建未观测状态变量和估计未知生物物理参数，对初始参数猜测不敏感。适用于Morris-Lecar模型、快慢放电/爆发模型、呼吸神经元模型。触发词：参数估计、状态重构、PINN、神经元模型、逆向问题、逆向建模、physics-informed neural network、parameter estimation、state reconstruction、Morris-Lecar。Votes: 0GitHub stars: 3
- Plastic Arbor SimulationPlastic Arbor突触可塑性模拟框架。从单个突触到形态神经元网络的可定制模拟，支持CPU/GPU高性能计算。适用于突触可塑性研究、神经网络模拟、形态学神经元建模。触发词：突触可塑性、Arbor、神经元模拟、形态学、synaptic plasticity、morphological neuron、simulation framework。Votes: 0GitHub stars: 3
- Platonic Representations Brain Universal GeometryPlatonic Representations in the Human Brain — self-supervised recovery of universal neural geometry across subjects using fMRI. Tests whether human visual cortex representations are approximately isometric and translatable via purely geometric transformations. Based on arXiv:2605.20496.Votes: 0GitHub stars: 3
- Platonic Representations Brain人脑柏拉图表征方法论：证明不同受试者的fMRI视觉表征在几何上近似等距，可通过无监督正交旋转进行跨个体翻译，无需配对数据或中间模型。适用于脑表征分析、个体间神经编码、fMRI分析。Votes: 0GitHub stars: 3
- Plug And Play Spiking OperatorsPlug-and-Play Spiking Operators: Breaking the Nonlinearity Bottleneck in Spiking Transformers. Research methodology from arXiv:2605.20289 (May 2026). A training-free ANN-to-SNN conversion framework that implements spike-friendly approximations for Transformer nonlinearities (Softmax, SiLU, normalization) using LIF neuron groups and lightweight bit-shift scaling. Use when working on: ANN-to-SNN conversion, spiking Transformers, neuromorphic LLM inference, spike-driven language models, or energ...Votes: 0GitHub stars: 3
- Pmnlv Neural CovariabilityPoisson Matrix-Normal Latent Variable (PMNLV) model for partitioning neural co-variability in population recordings. Extends single-neuron overdispersion to populations with Kronecker-factored covariance for structured gain-modulation analysis. Use when analyzing neural population co-variability, overdispersion in spiking data, Neuropixel recordings, structured gain covariance, or trial-to-trial variability beyond scalar Fano factor summaries. Activation: PMNLV, neural co-variability, overdis...Votes: 0GitHub stars: 3
- Polariton Bec Quantum NeuromorphicPolariton Bose-Einstein Condensate (BEC) theory for quantum neuromorphic computing. Covers polariton condensation, macroscopic quantum coherence at room temperature, driven-dissipative nonlinear dynamics, synchronization, pattern formation, and topological defects. Use when designing optical neural networks, quantum reservoir computing, room-temperature quantum simulators, or studying driven-dissipative quantum systems. arXiv: 2605.16256Votes: 0GitHub stars: 3
- Population Templatebased Brain Graph Augmentation For Improving Oneshot Learning Classification**arXiv ID:** 2212.07790 **Authors:** Oben Özgür, Arwa Rekik, Islem Rekik **Published:** 2022-12-14T14:56:00Z **Abstract:** The challenges of collecting medical data on neurological disorder diagnosis problems paved the way for learning methods with scarce number of samples. Due to this reason, one-shot learning still remains one of the most challenging and trending concepts of deep learning as it proposes to simulate the human-like learning approach in classification problems. Previous studi...Votes: 0GitHub stars: 3
- Position Hippocampal Explicit Memory Is The CornerDerived from arXiv:2606.11245 - Position: Hippocampal Explicit Memory Is the Cornerstone for AGIVotes: 0GitHub stars: 3
- Potre Test Time Reasoning Inspired By Cognitive HeSkill generated from arXiv paper 2607.20268: PoTRE: Test-Time Reasoning inspired by Cognitive HeterogeneityVotes: 0GitHub stars: 3
- Predictable Mean Field Chaos Random Recurrent NetworksPredictable mean-field chaos methodology for random recurrent networks - demonstrating that deterministic chaos is only apparently stochastic, with continuous past uniquely determining future trajectories.Votes: 0GitHub stars: 3
- Predictable Mean Field Chaos RnnPredictable Mean-Field Chaos in Random Recurrent Networks methodology — Krylov state space analysis revealing latent determinism in mean-field dynamics for analytic nonlinearities with fast Fourier decay. Demonstrates that microscopic sensitivity and predictive complexity are distinct aspects of chaos. Use when: analyzing RNN chaos, mean-field theory, Krylov complexity, Lyapunov exponents, Hamiltonian chaotic dynamics, classical dissipative systems, or prediction theory in recurrent networks.Votes: 0GitHub stars: 3
- Predictive Coding LightPredictive Coding Light+ (PCL+) methodology for spiking neural network sequence prediction. A spiking neural network architecture for unsupervised sequence processing that learns recurrent excitatory connections with delays to enable short-term retention of information. Combines spike timing-dependent plasticity (STDP) with synaptic delays to learn predictive representations. Successfully reproduces classic visual cortex sequence learning findings and learns to fill in missing inputs in gestu...Votes: 0GitHub stars: 3
- Predictive Subspace Recovery ProfilesTarget-Space Recovery Profiles methodology for evaluating model-brain alignment beyond prediction accuracy. Identifies which reproducible brain response dimensions are recovered by predictions, enabling diagnostic evaluation of NeuroAI model-brain alignment. Activation: model brain alignment, predictive subspace, recovery profile, brain prediction evaluation, NeuroAI alignment, target-space recovery, NSD analysis.Votes: 0GitHub stars: 3
- Primary Visual Cortex V1 FunctionsComprehensive framework for understanding V1 functions beyond feature detection - saccadic guidance, information bottleneck, and top-down support for visual recognition. Triggers: primary visual cortex, V1 functions, bottom-up saliency, saccadic guidance, information bottleneck.Votes: 0GitHub stars: 3
- Primate Ventral Visual Stream DynamicFramework for modeling temporal dynamics in the primate ventral visual stream across intrinsic dynamics, dynamic visual stimuli, and active sensing during eye movements. Activation: primate vision, ventral visual stream, VVS dynamics, active sensing.Votes: 0GitHub stars: 3
- Prior Elicitation ConnectivityBayesian prior elicitation methodology for single-subject functional connectivity network inference from resting-state fMRI. Introduces novel Bayesian priors on correlation matrices with a dedicated elicitation framework that translates expert beliefs about expected correlation levels and variability into interpretable hyperparameters. Provides distributional (not point) estimates of connectivity weights with uncertainty quantification and credible sets. Use when performing Bayesian functiona...Votes: 0GitHub stars: 3
- Prism Cross Subject Eeg EmotionPRISM framework for cross-subject EEG emotion recognition using prioritized channel importance and semi-supervised domain adaptation. Differentiable channel weighting via lightweight expert ensemble plus confidence-filtered pseudo-labels for label-efficient generalization across subjects. Activation: EEG emotion recognition, cross-subject BCI, channel selection, domain adaptation, PRISM, semi-supervised EEGVotes: 0GitHub stars: 3
- Prm Explainable Rnn P300 BciPost-Recurrent Module (PRM) for explainable RNN-based P300 classification in BCIs — combines performance improvement with global/local explainability techniques for transparent EEG-based neural decoding. Activation triggers: PRM, P300 BCI, explainable RNN, EEG explainability, post-recurrent module, P300 classification, transparent BCI.Votes: 0GitHub stars: 3
- Probegeometry Alignment Erasing The Crosssequence Memorization Signature Below Chance**arXiv ID:** 2605.01699 **Authors:** Anamika Paul Rupa, Anietie Andy **Published:** 2026-05-03T03:44:15Z **Abstract:** Recent attacks show that behavioural unlearning of large language models leaves internal traces recoverable by adversarial probes. We characterise where this retention lives and show it can be surgically removed without measurable capability cost. Our central protocol is a leave-one-out cross-sequence probe that tests whether a memorisation signature generalises across held-...Votes: 0GitHub stars: 3
- Projective Kolmogorov Arnold Neural Networks Pkans Entropydriven Functional Space Discovery For Interpretable Machine Learning**arXiv ID:** 2509.20049 **Authors:** Alastair Poole, Stig McArthur, Saravan Kumar **Published:** 2025-09-24T12:15:37Z **Abstract:** Kolmogorov-Arnold Networks (KANs) relocate learnable nonlinearities from nodes to edges, demonstrating remarkable capabilities in scientific machine learning and interpretable modeling. However, current KAN implementations suffer from fundamental inefficiencies due to redundancy in high-dimensional spline parameter spaces, where numerous distinct parameterisatio...Votes: 0GitHub stars: 3
- Prospect Theoryinspired Automated P2p Energy Trading With Qlearningbased Dynamic Pricing**arXiv ID:** 2208.12777 **Authors:** Ashutosh Timilsina, Simone Silvestri **Published:** 2022-08-26T16:45:40Z **Abstract:** The widespread adoption of distributed energy resources, and the advent of smart grid technologies, have allowed traditionally passive power system users to become actively involved in energy trading. Recognizing the fact that the traditional centralized grid-driven energy markets offer minimal profitability to these users, recent research has shifted focus towards dece...Votes: 0GitHub stars: 3
- Prospective Coding Path Integration Self Organizing--- created: 2026-06-16 arxiv_id: 2606.14649 authors: Facundo Emina, Emilio Kropff categories: q-bio.NC, cond-mat.dis-nn, nlin.AO published: 2026-06-12 activation: prospective coding, path integration, continuous attractor, self-organization, Hebbian plasticity, firing-rate adaptation, entorhinal cortex, neural dynamics ---Votes: 0GitHub stars: 3
- Psi Shared State Architecture V2PSI (Persistent Shared Interface): Shared-state architecture for coherent AI-generated instruments in personal AI agents. Transforms isolated AI-generated modules into persistent, connected, chat-complementary artifacts through a shared personal-context bus. Activation: PSI, shared state, personal AI, AI-generated instruments, coherent computing, context bus.Votes: 0GitHub stars: 3
- Psi Shared State ArchitecturePSI (Persistent Shared Interface): A shared-state architecture for coherent AI-generated instruments in personal AI agents. Enables cross-module reasoning and synchronized actions across interfaces. Use for: AI agent architecture, shared state design, personal AI systems, tool coordination, multi-module integration. Activation: PSI architecture, shared state, AI agent coordination, personal AI, cross-module reasoning.Votes: 0GitHub stars: 3
- Psm Quantum Memory DistributionProgressive Swapping to the Middle (PSM) protocol for entanglement distribution in quantum networks with imperfect quantum memories. Optimizes entanglement swapping order to minimize decoherence from finite memory coherence times. Presented at 2026 EuCNC and 6G Summit.Votes: 0GitHub stars: 3
- Psvit Structured Pruning Spiking VisionStructured pruning methodology for Spiking Vision Transformers (SViT) using uniform channel-wise filter pruning and sensitivity analysis for 22.4% memory savingVotes: 0GitHub stars: 3
- Psychiatric Triage Ai ChatbotsBenchmark evaluation of 15 frontier AI chatbots for psychiatric emergency triage using 112 clinical vignettes. Assesses accuracy, under-triage/over-triage rates, and risk-level specific performance. Triggers: psychiatric triage, emergency mental health, AI chatbot evaluation, clinical vignettes, suicide risk assessment.Votes: 0GitHub stars: 3
- Psychosis Scaling Critical Regime早期精神病临界区域内标度行为偏差研究。使用现象学重整化群(PRG)、功率谱密度(PSD)和去趋势波动分析(DFA)揭示精神病中的集体动力学重组而非简单临界态丢失。Votes: 0GitHub stars: 3
- Pulse Level QfmPulse-level Quantum Fourier Models (QFMs) for quantum machine learning. Optimizes variational quantum algorithms by using pulse parameters instead of gate-level angles, providing higher-dimensional escape routes in the optimization landscape. Use when: designing pulse-level quantum circuits, optimizing QFM training, improving variational quantum algorithm convergence, working with quantum machine learning expressibility and Fourier coefficient correlation, or replacing gate-level parameteriza...Votes: 0GitHub stars: 3
- Pulse Level Quantum ComputingPulse-level quantum computing skill — design, optimize, and analyze pulse-level variational quantum algorithms beyond the gate abstraction. Covers pulse parameterization, expressibility, Fourier coefficient correlation (FCC), composite gate sub-angle decomposition, and training landscape optimization. Use when: pulse-level quantum computing, variational quantum algorithms, quantum machine learning at pulse level, Fourier quantum models, QFM optimization, pulse parameterization, quantum compil...Votes: 0GitHub stars: 3
- Pulse Level Quantum Fourier ModelsPulse-level Quantum Fourier Models (QFMs) for quantum machine learning. Use when: (1) implementing variational quantum algorithms at the pulse/hardware level, (2) optimizing QFM training landscapes, (3) designing pulse-parameterized quantum circuits, (4) analyzing expressibility and Fourier coefficient correlation of quantum models, (5) replacing gate-level parameterization with pulse-level control. Activation: pulse-level quantum computing, quantum Fourier models, QFM training optimization, ...Votes: 0GitHub stars: 3
- Q Spirl Quantum Spiking RlQ-SpiRL: Quantum Spiking Reinforcement Learning framework combining spike-based temporal processing with variational quantum feature transformation for adaptive robot navigation and control. Use when: quantum reinforcement learning, spiking neural network RL, quantum spiking systems, robot navigation policies, quantum-enhanced SNN.Votes: 0GitHub stars: 3
- Qb Lif Quantized Burst Neurons V2Quantized Burst-LIF (QB-LIF) v2 - Enhanced learnable-scale quantized burst neurons for efficient Spiking Neural Networks. Adaptive spike count control with binary activation for hardware deployment. Keywords: SNN, burst neurons, quantization, neuromorphic hardware.Votes: 0GitHub stars: 3
- Qb Lif Quantized Burst NeuronsQuantized Burst-LIF (QB-LIF) neuron model with learnable-scale quantization for efficient Spiking Neural Networks (SNNs). Use when implementing energy-efficient deep SNNs with burst spiking, optimizing SNNs for short simulation horizons, or deploying SNNs on neuromorphic hardware. Provides learnable quantization scales, absorbable scale strategy for hardware efficiency, and ReLSG-ET surrogate gradient for stable training.Votes: 0GitHub stars: 3
- Qds Snn Quantum Deeply Supervised SpikingQuantum Deeply-Supervised Spiking Neural Network (QDS-SNN) methodology for energy-efficient traffic sign recognition. Integrates QNNs with SNNs using TSA-LIF neurons and QACM module, achieving 99.72% accuracy with 55.77% energy reduction.Votes: 0GitHub stars: 3
- Qif Neurons Superior Lif Gradient DescentQuadratic Integrate-and-Fire (QIF) neurons outperform LIF neurons in spike-based gradient descent with less fragmented loss landscapesVotes: 0GitHub stars: 3
- Qlam Quantum Attention MemoryQLAM: Quantum Long-Attention Memory methodology for long-sequence token modeling. Combines quantum linear algebra with attention mechanisms to overcome O(n²) scaling of transformer attention. Based on arXiv:2605.13833.Votes: 0GitHub stars: 3
- Qlif Cast Quantum Spiking ForecastingQuantum Leaky-Integrate-and-Fire (QLIF-CAST) methodology for time-series forecasting. Adapts QLIF spiking neural networks for multivariate regression, achieving 15.4% lower MSE than classical LIF and 94% faster convergence than QLSTM/QNN. Activated by: quantum spiking forecasting, QLIF, time-series quantum, quantum regression.Votes: 0GitHub stars: 3
- Qmaxcal Open Quantum ControlPath-space regularization for open quantum control using Girsanov's theorem. Use when designing quantum control policies under decoherence, computing trajectory likelihood ratios for monitored quantum systems, or optimizing open quantum system control via stochastic path integrals. Combines Girsanov change-of-measure with quantum trajectory theory for differentiable KL divergence estimation.Votes: 0GitHub stars: 3
- Qml Spiking EncodingSPATE: Spiking-Phase Adaptive Temporal Encoding for Quantum Machine Learning. Bridges neuromorphic computing with QML via spike-based temporal encoding into phase-encoded qubits. Use when: spiking quantum encoding, QML temporal encoding, spike encoding quantum, neuromorphic quantum computing, temporal data for QML, 脉冲量子编码.Votes: 0GitHub stars: 3
- Quacod Quantum Coordinate DescentQuantum Optimization via Coordinate Descent (QUACOD) methodology. Decomposes large-scale combinatorial optimization problems into quantum-solvable subproblems using coordinate descent, enabling NISQ-era hardware to handle problems 5-35x larger than direct quantum approaches. Use when: (1) optimization problems exceed available qubits, (2) scaling quantum optimization to practical problem sizes, (3) drone scheduling/logistics optimization, (4) iterative quantum-classical hybrid optimization wo...Votes: 0GitHub stars: 3
- Quantifying Entrainment Evidence A Comparison Of Frequentist AndQuantifying Entrainment Evidence: A Comparison of Frequentist and Bayesian Approaches for Information Processing Pathway Maps. Information Processing Pathway Maps (IPPMs) offer a scalable framework for formalizing the complex sequence of mathematical transformations applied to sensory stimuli. These maps chart the latency and... Activation: neural, neuroscience, inference, bayesian, interpretabilityVotes: 0GitHub stars: 3
- Quantization Snn Beyond AccuracyEarth Mover's Distance (EMD) methodology for evaluating SNN quantization beyond traditional accuracy metrics. Measures spike pattern distribution preservation for neuromorphic deployment. Keywords: SNN quantization, Earth Mover's Distance, spike pattern distribution, neuromorphic deployment, quantization evaluation.Votes: 0GitHub stars: 3