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Claude Skills by hiyenwong
github.com/hiyenwong9,934 skills5 installs19,223 views
- Stationary Covariance Spectra Neural DynamicsFree-probability framework for analyzing stationary covariance spectra in non-normal random recurrent neural networks. Derives closed functional equations for moment generating functions and analyzes tail eigenvalue behavior in critical regimes. arXiv:2606.31944Votes: 0GitHub stars: 3
- Stdp Spiking Transformer AttentionSpiking STDP Transformer (S²TDPT) implementing self-attention through spike-timing-dependent plasticity for energy-efficient neuromorphic computing with 88% energy reduction. Activation triggers: STDP transformer, spiking attention, neuromorphic transformer, bio-inspired attention, SNN transformer, energy-efficient attention.Votes: 0GitHub stars: 3
- Stdp Synaptic Delay LearningExtended STDP learning rule for simultaneously learning synaptic connection strengths and delays, validated on unsupervised SNN classification tasks with superior performance over delay-free STDP.Votes: 0GitHub stars: 3
- Stimulus Symmetries Rsm ConfoundStimulus symmetries can confound representational similarity analyses — demonstrates how stimulus symmetries in neural network inputs cause functionally-equivalent representations to produce different, drifting RSM geometries. Based on arXiv:2605.21324.Votes: 0GitHub stars: 3
- Stm Sequence Timing ReplaySpiking Temporal Memory (sTM) model for learning sequence timing and controlling replay speed via oscillatory background inputs. Provides biologically plausible mechanisms for encoding element-specific timing and flexible speed control.Votes: 0GitHub stars: 3
- Stochastic Graph Heat ConnectivityStochastic Graph Heat Modelling methodology for brain connectivity estimation. Uses noise-driven heat diffusion on graphs to estimate directed, multivariate, dynamic, model-based connectivity from neurophysiological data. Extends traditional coherence methods with graph-based PDE formulation and regularization. Activation: brain connectivity, graph heat modelling, neurophysiological data, directed connectivity, coherence, graph PDE, effective connectivityVotes: 0GitHub stars: 3
- Stochastic Momentum Tracking Push PullStochastic Momentum Tracking Push-Pull (SMTPP) algorithm for decentralized optimization over directed graphs. Decouples variance reduction from graph connectivity for robust convergence. Use for: directed graph optimization, decentralized learning, push-pull algorithms, network topology optimization, asymmetric communication. Activation: push-pull, directed graph optimization, SMTPP, momentum tracking, decentralized directed, asymmetric network.Votes: 0GitHub stars: 3
- Stochastic Quantum Neural NetworkStochastic Quantum Neural Network (SQNN) methodology for modeling neural dynamics using quantum superposition, entanglement, and unitary evolution beyond Von Neumann architecture constraints. Explores neuro-quantum correspondence between artificial neural networks and quantum systems. Use when: quantum neural network design, neuro-quantum modeling, stochastic quantum dynamics in AI, biological brain simulation with quantum computing, neural network architecture beyond Von Neumann. arXiv:2511....Votes: 0GitHub stars: 3
- Stochastic Resonance Tinnitus Review随机共振耳鸣模型十年综述。从幻听感知到适应性感官优化的神经计算理论，整合信息论、适应性信号检测、多通道听觉处理和跨模态可塑性。Votes: 0GitHub stars: 3
- Stochastic Synaptic Plasticity神经突触可塑性随机模型框架。基于STDP规则的突触权重演化数学模型，引入塑性核概念表示不同STDP规则，使用随机过程分析神经元-突触系统动力学。适用于计算神经科学、突触可塑性建模、STDP学习规则。触发词：突触可塑性、STDP、塑性核、突触权重、随机模型、synaptic plasticity、STDP、plasticity kernel、Hebbian learning。Votes: 0GitHub stars: 3
- Strand Survival Topological AnalysisSTRAND (Survival Topological Representation ANalysis of Diagrams) treats persistence diagrams as survival data for hypothesis testing, effect sizes, and vectorisation in neuroscience applications.Votes: 0GitHub stars: 3
- Structure Activity Nonlinear Spiking NetworksStructure-Activity in Nonlinear Spiking NetworksVotes: 0GitHub stars: 3
- Structured Convolution Matrices For Energyefficient Deep Learning**arXiv ID:** 1606.02407 **Authors:** Rathinakumar Appuswamy, Tapan Nayak, John Arthur, Steven Esser, Paul Merolla, Jeffrey Mckinstry, Timothy Melano, Myron Flickner, Dharmendra Modha **Published:** 2016-06-08T05:31:43Z **Abstract:** We derive a relationship between network representation in energy-efficient neuromorphic architectures and block Toplitz convolutional matrices. Inspired by this connection, we develop deep convolutional networks using a family of structured convolutional matrice...Votes: 0GitHub stars: 3
- Structured Light Turbulent Channel InformationAnalytical framework for structured light propagation through turbulent atmospheric channels using split-step mode-based approach. Power transfer between spatial modes scales linearly with distance, yielding matrix exponential solution for arbitrary propagation. Turbulence-spectrum spatial overlap determines transfer rates between mode pairs. Applies to free-space quantum optical communication, quantum key distribution networks, spatial mode multiplexing, and information-theoretic capacity an...Votes: 0GitHub stars: 3
- Structured Recurrent Snn Backprop FreeScalable learning in structured recurrent Spiking Neural Networks without backpropagation. Combines structured multi-layer recurrent SNN architecture with local plasticity mechanisms, WTA teaching signals, and three-factor learning rules for hardware-compatible SNN training. Based on Tang & Xie (2026), arXiv:2605.00402.Votes: 0GitHub stars: 3
- Stsbench Dorsal Stream Visual CortexSTSBench: A Large-Scale Dataset for Modeling Neuronal Activity in the Dorsal Stream of Primate Visual Cortex - Skill for understanding and applying the methods from arXiv:2607.15631Votes: 0GitHub stars: 3
- Stst Jepa Eeg FoundationSTST-JEPA: Shallow-Target Spatio-Temporal Joint Embedding Predictive Architecture for EEG self-supervised learning. Largest EEG foundation model (47,703 sessions, ages 5-81) using JEPA-style latent prediction with EMA tokenizer + auxiliary signal reconstruction. Rank 1 on NeuralBench for sex, age, psychopathology. Brain age gap correlates with cognitive efficiency. Activation: stst-jepa, eeg foundation model, brain age, self-supervised eeg, eeg self-supervised learning, JEPA eeg, EEG2Rep, bra...Votes: 0GitHub stars: 3
- Sub Bit Snn CompressionSub-bit quantization techniques for spiking neural networks to further reduce storage and computation beyond binary SNNs. Based on NeurIPS 2025 S2NN paper.Votes: 0GitHub stars: 3
- Subclinical Anxiety Brain NetworksMapping behavioral, physiological, and subjective components of subclinical anxiety to dissociable intrinsic brain networks using resting-state functional connectivity. Two-system framework with rsFC analysis. Trigger words: subclinical anxiety brain networks, rsFC anxiety, anxiety functional connectivity, ACC insula anxiety, hippocampus insula anxiety, threat anticipation anxiety, two-system anxiety framework.Votes: 0GitHub stars: 3
- Subconcussion Eeg Preconfiguration FailureEarly preconfiguration failure detection methodology for repetitive subconcussive (rSC) brain injuries using high-density EEG. Captures millisecond-level cortical dynamics and spatiotemporal features for sports neurology and concussion screening. Activation: subconcussion, EEG, sports neurology, concussion detection, brain injury.Votes: 0GitHub stars: 3
- Subcortical Shape Cognition AgingSubcortical shape variations and their associations with cognition across the 8th decade of life. Longitudinal study using neuroimaging and cognitive data from Lothian Birth Cohort 1936. Analyzes heterogeneous morphological trajectories in hippocampus, thalami, globus pallidi, and ventral DC. Uses ANCOVA and mixed linear model analyses to investigate vertex displacement patterns associated with cognitive aging. Use when studying brain morphology changes, subcortical shape analysis, cognitive ...Votes: 0GitHub stars: 3
- Subject Level Heterogeneity Eeg Motor ImageryLarge-scale benchmark methodology for EEG motor imagery decoding that addresses subject-level heterogeneity through portfolio-based pipeline selection. Use when analyzing inter-individual variability in EEG BCI systems, comparing covariance tangent-space projection (cov-tgsp) vs Common Spatial Patterns (CSP), or designing personalized motor imagery decoding pipelines.Votes: 0GitHub stars: 3
- Sumo Whole Body LocomanipulationSim-to-real approach for dynamic whole-body loco-manipulation with test-time steering. Use when: (1) Designing legged robot control systems, (2) Implementing dynamic manipulation with heavy objects, (3) Building generalizable locomotion policies, (4) Transferring simulation-trained policies to real robots. Triggers: legged robots, whole-body control, loco-manipulation, sim-to-real transfer, dynamic manipulation, test-time steering, policy generalization.Votes: 0GitHub stars: 3
- Superconducting Neuron NeuromorphicProgrammable superconducting neuron with intrinsic in-memory computation and dual-timescale plasticity for ultra-efficient neuromorphic computing using Josephson junctions.Votes: 0GitHub stars: 3
- Supervised Memory TrainingSupervised Memory Training (SMT) methodology for parallel RNN pretraining without backpropagation through time.Votes: 0GitHub stars: 3
- Suprasnn Synapse Level Parallel Snn AcceleratorSupraSNN 是一种受超标量处理器架构启发的 SNN 加速器，通过硬件-软件协同设计实现突触级并行计算。核心创新：将突触事件视为可并行化的微操作，物理解耦突触和神经元计算单元。Votes: 0GitHub stars: 3
- Surface Code Lattice SurgerySuperconducting surface-code processor with lattice-surgery logical operations — experimental demonstration of fault-tolerant logical Bell state preparation, Deutsch-Jozsa algorithm, and magic-state injection for non-Clifford rotations. Logical gate fidelity 0.943 for RX(π/4).Votes: 0GitHub stars: 3
- Surrogate Gradient Snn TrainingSurrogate Gradient Learning for Spiking Neural Networks - comprehensive training framework using differentiable surrogate functions to overcome the non-differentiability of spike functions. Includes multiple surrogate gradient types (fast-sigmoid, exponential, arctan, erf), temporal batch normalization, neuron normalization, and advanced training strategies for deep SNNs. Activation: surrogate gradient SNN, differentiable spike, spiking neural network training, SNN backpropagation, time surro...Votes: 0GitHub stars: 3
- Svp Lattice Tail BoundRogers tails methodology for the lattice gamma parameter — uniform random-lattice tail bounds for the SVP kissing-profile parameter. Provides probabilistic guarantees for lattice-dependent parameters in quantum and classical SVP algorithms. Activation: SVP algorithm, shortest vector problem, lattice kissing number, Rogers mean value, lattice tail bound, quantum lattice algorithm, gamma parameter.Votes: 0GitHub stars: 3
- Swpc Directed Functional ConnectivitySliding-window prediction correlation (SWpC) for time-varying directed functional connectivity. Embeds directional LTI models within sliding windows to estimate time-resolved information flow in brain networks, going beyond undirected correlation.Votes: 0GitHub stars: 3
- Symboliclight Spike Gated LanguageSymbolicLight V1: Spike-Gated Dual-Path Language Modeling with High Activation Sparsity and Sub-Billion-Scale Pre-Training Evidence. Research methodology from arXiv:2605.21333 (May 2026). First natively trained spiking language model combining binary LIF spike dynamics with continuous residual stream. Dual-Path SparseTCAM module replaces dense self-attention. 194M params, >89% activation sparsity. Use when working on: spiking language models, energy-efficient LLMs, spike-driven NLP, neuromorp...Votes: 0GitHub stars: 3
- Synaptic Matrix Eigenvalue Analysis"突触矩阵特征值分析方法论。研究稀疏连接神经网络中突触矩阵的谱行为，Votes: 0GitHub stars: 3
- Synaptic Matrix Eigenvalues AnalysisSpectral analysis of synaptic matrix eigenvalues for stability, transient dynamics, and memory capacity analysis in sparsely connected neural networksVotes: 0GitHub stars: 3
- Synaptic Motif Mean FieldMean-field theory bridging microscale synaptic motifs to macroscale heterogeneous neural dynamics. Derives low-rank equations for P-population networks where chain motifs induce correlations in synaptic variability, enabling microscopic fluctuations to influence mesoscopic dynamics. Requires only 2P latent variables. Use when: modeling brain circuits with fine-scale connectivity, deriving mean-field equations for heterogeneous populations, reverse-engineering connectivity from neural recordings.Votes: 0GitHub stars: 3
- Synaptic Motifs Mean Field DynamicsMean-field theory linking microscale synaptic motifs to macroscale neural population dynamics. Phenomenological framework integrating connectivity, synaptic transmission, plasticity, and heterogeneity.Votes: 0GitHub stars: 3
- Synaptic Motifs Mean FieldMean-field theory linking microscale synaptic motifs to macroscopic heterogeneous population dynamics. Bridges synaptic-resolution connectomics with nonlinear neural dynamics via low-rank mean-field equations. Applicable to RNN analysis, neural population modeling, V1 response prediction.Votes: 0GitHub stars: 3
- Synaptic Weight Distributions Plasticity GeometrySynaptic Weight Distributions and Plasticity GeometryVotes: 0GitHub stars: 3
- Synchronization Bipartite Oscillator NetworksResearch on Kuramoto Sakaguchi model applied to bipartite networks (excitatory/inhibitory populations), revealing rich collective dynamics including both continuous and discontinuous transitions from full synchrony to partial synchrony (PS). The PS state constitutes an example of **self-organized quasiperiodicity** in the canonical Kuramoto Sakaguchi model despite its purely linear global coupling.Votes: 0GitHub stars: 3
- Syndrome Resampling QecSyndrome resampling methodology for enhancing quantum error correction thresholds. Increases QEC thresholds of any decoder and suppresses logical errors without additional hardware by biasing syndrome averages towards most likely syndromes. Establishes connection between Rényi coherent information and syndrome probability distribution. Activation: syndrome resampling, QEC threshold, Rényi coherent information, decoder-agnostic QEC, logical fidelity improvement, syndrome biasing.Votes: 0GitHub stars: 3
- Synthesizing The Preferred Inputs For Neurons In Neural Networks Via Deep Generator Networks**arXiv ID:** 1605.09304 **Authors:** Anh Nguyen, Alexey Dosovitskiy, Jason Yosinski, Thomas Brox, Jeff Clune **Published:** 2016-05-30T16:22:54Z **Abstract:** Deep neural networks (DNNs) have demonstrated state-of-the-art results on many pattern recognition tasks, especially vision classification problems. Understanding the inner workings of such computational brains is both fascinating basic science that is interesting in its own right - similar to why we study the human brain - and will en...Votes: 0GitHub stars: 3
- Synthetic Biological IntelligenceSynthetic Biological Intelligence (SBI) methodology — engineered systems where living Biological Neural Networks (BNNs) are interfaced with hardware/software for task-oriented information processing. Combines organoid technology, MEAs, neuromorphic computing, and ML. Activation: SBI, organoid intelligence, biological neural network, bio-digital interface, wetware computing.Votes: 0GitHub stars: 3
- Synthetic Data Generation By Supervised Neural Gas Network For Physiological Emotion Recognition Data**arXiv ID:** 2501.16353 **Authors:** S. Muhammad Hossein Mousavi **Published:** 2025-01-19T15:34:05Z **Abstract:** Data scarcity remains a significant challenge in the field of emotion recognition using physiological signals, as acquiring comprehensive and diverse datasets is often prevented by privacy concerns and logistical constraints. This limitation restricts the development and generalization of robust emotion recognition models, making the need for effective synthetic data generation ...Votes: 0GitHub stars: 3
- Tablet Fmri Tokenization TransformerTABLeT framework for fMRI volume tokenization using pre-trained 2D natural image autoencoders, enabling long-range spatiotemporal dynamics modeling with Transformer encoders. Activation triggers: fMRI tokenization, brain transformer, TABLeT, neuroimaging autoencoder, long-range brain dynamics, UK Biobank fMRI.Votes: 0GitHub stars: 3
- Tackling Sequence To Sequence Mapping Problems With Neural Networks**arXiv ID:** 1810.10802 **Authors:** Lei Yu **Published:** 2018-10-25T09:24:13Z **Abstract:** In Natural Language Processing (NLP), it is important to detect the relationship between two sequences or to generate a sequence of tokens given another observed sequence. We call the type of problems on modelling sequence pairs as sequence to sequence (seq2seq) mapping problems. A lot of research has been devoted to finding ways of tackling these problems, with traditional approaches relying on a c...Votes: 0GitHub stars: 3
- Taco Tail Aware Credit CalibrationFixes the "Positive-Credit Contamination" failure mode in critic-free RL methods like GRPO.Votes: 0GitHub stars: 3
- Target Space Recovery Profiles Brain AlignmentBeyond Prediction Accuracy: Target-Space Recovery Profiles for Evaluating Model-Brain Alignment — a framework for identifying which reproducible brain response dimensions are recovered by model predictions, going beyond simple prediction accuracy. (arXiv:2605.20127)Votes: 0GitHub stars: 3
- Task Aware Brain Connectivity任务感知有效脑连接学习方法论（TBDS）。使用DAG学习框架从fMRI时间序列构建任务相关的脑网络，结合图神经网络进行下游预测任务。适用于fMRI分析、脑网络建模、精神疾病诊断预测。触发词：有效连接、脑网络、fMRI分析、DAG学习、图神经网络、task-aware connectivity、brain network、effective connectivity。Votes: 0GitHub stars: 3
- Taskdriven Convolutional Recurrent Models Of The Visual System**arXiv ID:** 1807.00053 **Authors:** Aran Nayebi, Daniel Bear, Jonas Kubilius, Kohitij Kar, Surya Ganguli, David Sussillo, James J. DiCarlo, Daniel L. K. Yamins **Published:** 2018-06-20T20:27:23Z **Abstract:** Feed-forward convolutional neural networks (CNNs) are currently state-of-the-art for object classification tasks such as ImageNet. Further, they are quantitatively accurate models of temporally-averaged responses of neurons in the primate brain's visual system. However, biological vis...Votes: 0GitHub stars: 3
- Tau Bno Brain Neural OperatorBrain Neural Operator (Tau-BNO) surrogate framework for rapidly approximating Network Transport Model dynamics of pathological tau protein spread in Alzheimer's disease. Combines function operator encoding kinetic parameters with query operator preserving initial state, using spectral kernel for anisotropic transport. Activation triggers: tau propagation, alzheimer modeling, neural operator, brain network transport, biophysical surrogate, disease progression modeling.Votes: 0GitHub stars: 3
- Tea Nets Cognitive NetworkTEA Nets (Target-Event-Agent Networks) — computational framework combining AI and cognitive network science to extract subjects (Agents), verbs (Events), and objects (Targets) from text. Enables interpretable emotion detection, semantic frame analysis, and linguistic inquiry. Activation: TEA Nets, cognitive network science, semantic network extraction, agent-event-target, text network analysis.Votes: 0GitHub stars: 3