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Showing 10,657–10,680 of 21,409 skills
- Synaptic Motifs Mean Field TheoryMean-field theory for heterogeneous synaptic motifs in multi-population neural networks. Bridges microscale synaptic connectivity (second-order motifs) to macroscale population dynamics. Activation: synaptic motifs, mean-field theory, heterogeneous dynamics, multi-population networks, connectomics.Votes: 0GitHub stars: 3
- Synaptic Motifs Mean Field DynamicsMean-field theory linking microscale synaptic motifs to macroscopic heterogeneous population dynamics in neural networks. Use when studying synaptic-resolution connectomics, second-order motifs, random RNNs with cell types, or heterogeneous population dynamics.Votes: 0GitHub stars: 3
- Synaptic Motifs Heterogeneous DynamicsMean-field theory bridging microscale synaptic motifs to macroscale heterogeneous population dynamics in neural networksVotes: 0GitHub stars: 3
- Synaptic Delays Oscillatory Ei NetworksSynaptic delays in oscillatory E-I networks.Votes: 0GitHub stars: 3
- Synaptic Clustering Covariance DiscriminationSynaptic clustering methodology for learning covariance structure discrimination using Dendrinet architecture with hierarchical dendritic segments and sparse conductance-based synapses. Use when analyzing functional synapse clusters (FSCs), dendritic nonlinearities, synaptic structural plasticity, or covariance classification tasks in computational neuroscience.Votes: 0GitHub stars: 3
- Survey On Neural Routing Solvers**arXiv ID:** 2602.21761 **Authors:** Yunpeng Ba, Xi Lin, Changliang Zhou, Ruihao Zheng, Zhenkun Wang, Xinyan Liang, Zhichao Lu, Jianyong Sun, Yuhua Qian, Qingfu Zhang **Published:** 2026-02-25T10:24:43Z **Abstract:** Neural routing solvers (NRSs) that leverage deep learning to tackle vehicle routing problems have demonstrated notable potential for practical applications. By learning implicit heuristic rules from data, NRSs replace the handcrafted counterparts in classic heuristic frameworks,...Votes: 0GitHub stars: 3
- Supervised Training Rapidly Degrades Early VisualDerived from arXiv:2605.30556 - Supervised Training Rapidly Degrades Early Visual Cortex Alignment Across Biologically Plausible Learning RulesVotes: 0GitHub stars: 3
- Stsbench DatasetA skill for understanding and using the STSBench dataset for modeling neuronal activity in the dorsal stream of primate visual cortex. Based on arXiv:2607.15631.Votes: 0GitHub stars: 3
- Structagentharnesslong HorizondigitalagentswithuniResearch paper: StructAgent: Harness Long-horizon Digital Agents with Unified Causal Structure. Brief summary of key findings and contributions.Votes: 0GitHub stars: 3
- Stimulus Evoked Network Dynamics OrganoidsGraph-computational framework for analyzing stimulus-evoked propagation dynamics in human cortical organoids using HD-MEA recordings. Includes stimulus-conditioned functional graphs, graph-constrained dynamical models, biological message-passing principles, and longitudinal depression analysis.Votes: 0GitHub stars: 3
- Statistical Validation In Cultural Adaptations Of Cognitive Tests A Multi Regional Systematic Review**arXiv ID:** 2504.13495 **Authors:** Miit Daga, Priyasha Mohanty, Ram Krishna, Swarna Priya RM **Published:** 2025-04-18T06:25:02Z **Abstract:** This systematic review discusses the methodological approaches and statistical confirmations of cross-cultural adaptations of cognitive evaluation tools used with different populations. The review considers six seminal studies on the methodology of cultural adaptation in Europe, Asia, Africa, and South America. The results indicate that proper adapt...Votes: 0GitHub stars: 3
- Stationary Covariance Spectra Non Normal DynamicsFree probability framework for analyzing stationary covariance spectra in discrete-time non-normal random recurrent neural networks. Provides closed-form functional equations for eigenvalue distributions and critical regime behavior.Votes: 0GitHub stars: 3
- Spiking Neural Network AnalysisAnalyze Spiking Neural Network (SNN) papers, extract technical patterns from knowledge graph, and identify reusable research methodologies for neuromorphic computing.Votes: 0GitHub stars: 3
- Spikedecoder Snn Gpt ArchitectureSpikeDecoder is the first fully spiking neural network (SNN) implementation of the Transformer decoder architecture for natural language processing, achieving 87-93% theoretical energy reduction compared to ANN baselines.Votes: 0GitHub stars: 3
- Spectral Surgery Quantum State TransferSpectral surgery methodology for high-fidelity quantum state transfer in XX spin chains. Analytic construction that interpolates between Krawtchouc (perfect transfer) and homogeneous chains, capping coupling strengths while maintaining high transfer fidelity. (arXiv:2412.02321)Votes: 0GitHub stars: 3
- Spectral Phase Transitions Nn LearningSpectral phase transitions and trainability in neural network learning dynamics methodology. Formulates NN training as stochastic evolution of random matrix ensembles, showing BBP (Baik-Ben Arous-Péché) transitions during SGD where isolated eigenvalues detach from random bulk. Derives phase diagram of trainability governed by step size and initial weight variance. Links spectral analysis to representation formation, optimisation hyperparameters, and generalization. Use when: analyzing neural ...Votes: 0GitHub stars: 3
- Spectral Ot Functional AlignmentSpectralOT — a geometry-aware, spectral optimal-transport functional alignment method for fMRI that embeds cortical surface geometry (Laplace-Beltrami eigenmodes) into the alignment cost to regularize cross-subject alignment while preserving anatomical structure. Use when building population-level brain decoders, doing cross-subject fMRI alignment, or need a fast geometry-preserving alternative to Hyperalignment / Riemannian alignment. Trigger words: functional alignment, cross-subject decodi...Votes: 0GitHub stars: 3
- Spatial Neighboring Scattering TransformSpatial Neighboring Scattering Transform (SNST) — a wavelet-scattering-based cross-channel amplitude-coupling measure for EEG connectivity that captures amplitude-envelope and cross-frequency coupling, robust to volume conduction where phase-sync fails. Use when building EEG/fNIRS functional connectivity pipelines, comparing against PLI/wPLI, or extracting inter-regional amplitude-domain dependence.Votes: 0GitHub stars: 3
- Sparse Neural Connectivity RecoveryCovariance-based method with Granger-causality refinement for recovering sparse neural connectivity from partial measurementsVotes: 0GitHub stars: 3
- Sound Localization Equilibrium DynamicsMicrosecond-precision sound localization emerges from slow equilibrium dynamics. ITD represented as stable equilibrium of neural population dynamics rather than classical Jeffress place-coding framework.Votes: 0GitHub stars: 3
- Soliton Waves Wstdp SnnSoliton-like wave propagation in recurrent spiking neural networks with weighted STDP. Use when studying cortical traveling waves, activity zone delimitation, spatial memory formation, or self-propagating neural activity patterns.Votes: 0GitHub stars: 3
- Snn Sequence Timing Replay SpeedSpiking Temporal Memory (sTM) model for learning sequence timing and flexible replay speed control - biologically plausible timing encoding via oscillatory modulationVotes: 0GitHub stars: 3
- Snn Online Data Reduction PhysicsSpiking Neural Networks for online data reduction in high-energy physics detectors. Temporal-coincidence encoding and distributed SNN architecture for the ePIC dRICH detector at the Electron-Ion Collider. Achieves 5x data reduction while preserving genuine Cherenkov photon signals against SiPM dark counts.Votes: 0GitHub stars: 3
- Small Free And Effective Orchestrating Open WeightSkill generated from arXiv paper 2607.20216: Small, Free, and Effective: Orchestrating Open-Weight Small Language Models to Outperform Single LLM for Malware AnalysisVotes: 0GitHub stars: 3