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Research, evidence gathering, literature, reports, investigation, and synthesis
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Showing 11,281–11,304 of 21,409 skills
- Neuromimetic Perceptual CompressionBrain-inspired perceptual compression using evidence-driven neuromimetic principles. Leverages human visual system characteristics for efficient data compression that prioritizes perceptually important information.Votes: 0GitHub stars: 3
- Neuroflownet Scalp To IeegNeuroFlowNet — cross-modal generative framework using Conditional Normalizing Flow for reconstructing high-fidelity iEEG signals from non-invasive scalp EEGVotes: 0GitHub stars: 3
- Neurodegenerative 4d Diffusion V34D(3D×T)扩散模型用于神经退行性疾病脑解剖结构的纵向生成建模。基于形变的形态测量学(DBM)和平稳速度场(SVF)。Votes: 0GitHub stars: 3
- Neurodegenerative 4d Diffusion V24D (3D×T) diffusion-based generative framework for modeling neurodegenerative brain anatomy progression. Combines spatial and temporal modeling for longitudinal brain imaging and disease progression prediction. Keywords: neurodegenerative disease, 4D diffusion model, longitudinal brain imaging, brain anatomy modeling, disease progression prediction, generative AI.Votes: 0GitHub stars: 3
- Neurocybernetic Large Scale NeuroscienceIntegrative neurocybernetic modeling framework for large-scale neuroscience. Unifies diverse neural datasets across animals, brain areas, and behaviors through cybernetic principles. Addresses fragmentation in computational neuroscience. Keywords: neurocybernetics, large-scale neuroscience, integrative modeling, cross-species, unified framework.Votes: 0GitHub stars: 3
- Neurocogmap Llm Cognitive OrganizationNeuroCogMap framework for mapping cognitive organization in LLMs using neuroscience-inspired methods, linking LLM internal representations to human cortical functionVotes: 0GitHub stars: 3
- Neuroai Beyond Bridging Neuroscience AiNeuroAI research roadmap bridging neuroscience and AI - identifies three fundamental capabilities for AI advancement: world modeling, motor control, and biological learning. NSF workshop framework for interdisciplinary research. Keywords: NeuroAI, world models, motor control, biological learning, neuroscience-AI integration.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 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
- 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
- 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
- Neural Tracking Correlation InterpretationNeural tracking correlation interpretation with null dist.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 Receptive Fields Hyperbolic GeometryNeural Receptive Fields via Hyperbolic GeometryVotes: 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 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 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 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 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 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 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 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 Emulator TheoryNeural Emulator TheoryVotes: 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