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Research, evidence gathering, literature, reports, investigation, and synthesis
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Showing 11,881–11,904 of 21,385 skills
- Brain Brainstorming Generative ModelsGenerative models for brain "brainstorming" — studying the brain's spontaneous idea generation using free energy principle, critical dynamics, and default mode network analysis. Based on Smith et al. (2026) linking neural criticality, predictive coding, and spontaneous thought patterns. Triggers: brain brainstorming, spontaneous thought, neural idea generation, predictive coding creativity, free energy imagination, default mode network generative, 脑风暴生成模型, 自发思维生成Votes: 0GitHub stars: 3
- Brain Alignment Vlm Lam GameplayBrain alignment of vision-language models (VLMs) and large-action models (LAMs) with fMRI during naturalistic gameplay. Use when: studying brain-AI alignment during interactive tasks, comparing VLMs vs LAMs neural encoding, analyzing action vs reasoning representations in frontal-parietal cortex, or designing fMRI encoding studies with foundation models.Votes: 0GitHub stars: 3
- Bounded Degree Max Linsat DqiApproximability limits for bounded-degree max-LINSAT and implications for decoded quantum interferometryVotes: 0GitHub stars: 3
- Bosonic Grid States QecBosonic quantum error correction using Gottesman-Kitaev-Preskill (GKP) grid states and programmable nonlinear bosonic circuits. Covers grid state preparation, logical qubit encoding in continuous-variable oscillators, Wigner function characterization, and fault-tolerant bosonic QEC architectures. Use when working on: bosonic codes, GKP states, cat/qubit encodings, continuous-variable QEC, nonlinear bosonic gates, or oscillator-based quantum computing. Triggers: bosonic QEC, GKP grid states, c...Votes: 0GitHub stars: 3
- Boosting Brain To Image Tribe V2TRIBE v2 data augmentation methodology for brain-to-image decoding. Uses pretrained encoding model on 1000+ hours of video/audio/language fMRI to generate synthetic data, achieving 68% improvement in Top-10 retrieval accuracy. Supports zero-shot decoding when trained exclusively on synthetic fMRI.Votes: 0GitHub stars: 3
- Bleg Llm Brain Graph EnhancerBLEG (LLM-Enhanced Brain Graph Analysis) methodology. Integrates LLMs with brain graph neural networks for improved neurological disease classification via knowledge-enhanced connectivity representation.Votes: 0GitHub stars: 3
- BiomysterybenchBioMysteryBench methodology for benchmarking LLM bioinformatics research capabilities on real-world datasets with consensus-based grading and path-independent evaluationVotes: 0GitHub stars: 3
- Binary Spiking Causal ModelsCausal analysis of Binary Spiking Neural Networks (BSNNs) using logic-based explainable AI methods. Formally defines BSNNs as binary causal models and provides tractable algorithms for computing abductive explanations.Votes: 0GitHub stars: 3
- Bimoe Brain Inspired Experts EegBrain-Inspired Mixture of Experts (BiMoE) framework for EEG-dominant affective state recognition. Uses brain-topology-aware expert partitioning with dual-stream encoders and adaptive routing for multimodal sentiment analysis combining EEG with peripheral physiological signals. Activation: BiMoE, brain-inspired MoE, EEG affective recognition, multimodal sentiment analysis, topology-aware experts, physiological signal fusion.Votes: 0GitHub stars: 3
- Beyond Neural Activity PredictionMulti-level representational probing framework for evaluating digital twins of sensory cortex beyond standard prediction accuracy. Probes latent representations (linear decodability, latent-unit tuning, population geometry) in mouse V1 digital twins. Based on arXiv:2605.23122 (May 2026). Use when evaluating brain digital twins, comparing model architectures for neural prediction, or studying latent representations in vision models.Votes: 0GitHub stars: 3
- Behavior Vlm NeuroscienceFinetuning-free behavioral understanding framework for neuroscience using vision-language models. Enables pose estimation and behavioral analysis linking neural activity to natural actions without human annotation. Use when: analyzing animal behavior from video, building neuroscience behavioral pipelines, or doing finetuning-free VLM behavioral understanding.Votes: 0GitHub stars: 3
- Bci Rehabilitation ProtocolsOptimized BCI rehabilitation protocols for stroke recovery. Addresses task design, training duration, and neuroplasticity-driven adaptation for maximizing post-stroke motor recovery through brain-computer interfaces.Votes: 0GitHub stars: 3
- Bayesian Ippm Cortical EntrainmentBayesian framework for Information Processing Pathway Maps (IPPMs) to map cortical entrainment from EEG/MEG data. Compares Bayesian vs frequentist approaches for model adjudication in computational neuroscience. Uses temporal response functions (TRFs) and model evidence for reconstructing sensory processing pathways. Activation: IPPM, cortical entrainment, Bayesian model comparison, information processing pathway, temporal response function, auditory processing, EEG MEG analysis, model adjudi...Votes: 0GitHub stars: 3
- Bayesian Information Processing Pathway MapsBayesian framework for quantifying neural entrainment evidence in Information Processing Pathway Maps (IPPMs). Shifts from frequentist hypothesis testing to probabilistic model adjudication, enabling relative evidence quantification for competing computational hypotheses. Applied to auditory neuroimaging for reconstructing cortical processing pathways.Votes: 0GitHub stars: 3
- Bara Bayesian Adaptive Rank LoraBaRA — Bayesian Adaptive Rank Allocation for LoRA fine-tuning. Dynamically allocates per-instance effective rank via sparse activation of disentangled latent factors, with complexity-theoretic generalization bounds depending on learned joint effective rank rather than max rank.Votes: 0GitHub stars: 3
- Bandwidth Reduction Packetized MpcBandwidth reduction methods for packetized Model Predictive Control over lossy networks. Multi-horizon MPC formulation with communication-rate reduction for networked control systems. Use for: networked MPC, bandwidth-efficient control, 5G/IoT control systems, packetized control, offloaded MPC. Activation: packetized MPC, bandwidth reduction, networked control, multi-horizon MPC, lossy network control.Votes: 0GitHub stars: 3
- Backpropagation Brain MisalignmentBackpropagation algorithm misalignment with human brain visual processing hierarchy research. Uses fMRI/MEG to map backpropagated gradients onto neural data, showing DINOv3 gradients can predict brain signals but spatial/temporal organization diverges from biologically plausible backpropagation. arXiv: 2605.28693. Activation: backpropagation brain alignment, gradient neural correspondence, encoding analysis backprop, DINOv3 brain mapping, biological backpropagation, fMRI gradient mappingVotes: 0GitHub stars: 3
- Backpropagation Brain Hierarchy Misalignment反向传播算法与人脑视觉处理层级的不匹配研究。使用fMRI和MEG证明梯度虽能预测脑信号，但其时空组织与生物学反向传播机制不符。激活词：反向传播、大脑层级、brain hierarchy、backpropagation、fMRI、MEG、视觉处理、DINOv3、神经网络学习机制。Votes: 0GitHub stars: 3
- Backprop Brain Hierarchy MisalignmentMethodology for analyzing the misalignment between backpropagation algorithms in deep neural networks and the hierarchical organization of brain responses. Extends encoding analyses from forward activations to backpropagated gradients, revealing fundamental differences in learning mechanisms. Use when studying brain-DNN alignment, computational neuroscience, or investigating whether backpropagation is biologically plausible.Votes: 0GitHub stars: 3
- Autoregressive Flow Matching Neural DynamicsFramework for probabilistic prediction of neural population dynamics using autoregressive flow matching (AFM). Addresses the inherent stochasticity and nonlinearity of neural activity by leveraging transport-based generative modeling to forecast neural responses from multimodal sensory input at scale.Votes: 0GitHub stars: 3
- Automated Neural Characterization LanguageAutomated neural characterization using natural language and digital twins. Closed-loop framework that translates neuron activation patterns into concise semantic descriptions, generates hypothesis images, and verifies them in silico. Use when studying: neural selectivity characterization, digital twin neuroscience, semantic hypothesis testing, V1/V4 visual cortex encoding, generative models for neural decoding, or combining language models with neural data. arXiv: 2605.12485 (q-bio.NC, q-bio...Votes: 0GitHub stars: 3
- Autocog Automated Cognitive ScientistPaper analysis: AutoCog — an automated LLM-driven system for discovering cognitive theories. The system takes existing theories as seeds, generates novel hypotheses, tests them against data, and produces executable cognitive models. Validated with human participants showing superior performance over established theories. Source: arXiv:2606.26693 (q-bio.NC, cs.AI), 2026-06-24. Activation keywords: AutoCog, automated theory discovery, cognitive science, LLM-driven science, computational cogniti...Votes: 0GitHub stars: 3
- Auto Dsm Evaluation FrameworkBlack-box evaluation framework for assessing LLM-generated Design Structure Matrices (DSMs) from structured technical documentation. Integrates structural metrics (Completeness, Correctness, Coupling Density), classification metrics (Selective Accuracy, Abstention Coverage), and stability measures (Entropy, Fleiss' κ) into a Composite Quality Score (Q). Provides transparent benchmarking methodology for auditing Auto-DSM pipelines in MBSE workflows.Votes: 0GitHub stars: 3
- Attention Task Structure Cognitive FlexibilityAttention to task structure for cognitive flexibility — neural mechanisms enabling flexible switching between task rules. Demonstrates how attentional mechanisms gate task-relevant information for cognitive control. Applicable to cognitive neuroscience, neural network design, cognitive flexibility, attention mechanisms. 触发词: cognitive flexibility, attention to structure, task switching, cognitive control, attentional gatingVotes: 0GitHub stars: 3