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Showing 11,977–12,000 of 21,385 skills
- Memex RlMemex(RL)Votes: 0GitHub stars: 3
- Vencircuit Ven Gradient ScaffoldVENCircuit methodology — Von Economo neurons as residual gradient scaffolds in recurrent spiking neural networks for reliable social skill acquisition. Use when researching: Von Economo neurons (VENs), spiking neural networks for social cognition, gradient flow in recurrent networks, residual connections in SNN, training convergence stability, autism spectrum conditions (ASC) computational models, frontotemporal dementia (bvFTD) cellular basis. Keywords: Von Economo neurons, spiking neural ne...Votes: 0GitHub stars: 3
- Untrained Cnns Backpropagation V1 Rsa系统RSA比较研究：展示未训练CNN在V1视觉皮层区域与反向传播训练的CNN具有相似表征。通过大规模fMRI和表征相似性分析，挑战传统深度学习需要大量训练的观点。适用于视觉皮层建模、CNN可解释性、神经科学。Votes: 0GitHub stars: 3
- Triple Loop Consolidation Non Gradient MemoryTriple-Loop Consolidation methodology for persistent memory in non-gradient dissipative cognitive architectures. Deep Memory (DM) operates through recording-seeding-reentry cycle. Discrete MoE routing is causally prerequisite. Activation: triple-loop consolidation, non-gradient memory, dissipative cognitive architecture, memory stability, continual learning without backprop.Votes: 0GitHub stars: 3
- Tribe V2 Trimodal Foundation ModelTRIBE v2 tri-modal foundation model methodology for in-silico neuroscience. Uses video, audio, and language modalities to predict human brain activity across naturalistic and experimental conditions. Supersedes linear encoding models with several-fold accuracy improvements. Enables in-silico experimentation and reveals multisensory integration topography. Activation: TRIBE v2, brain foundation model, in-silico neuroscience, multi-modal brain prediction, fMRI encoding model, multisensory integ...Votes: 0GitHub stars: 3
- Tribe V2 Foundation ModelTRIBE v2 tri-modal foundation model methodology for in-silico neuroscience. Uses video/audio/language embeddings to predict whole-brain fMRI across 720 subjects.Votes: 0GitHub stars: 3
- Texture Misalignment Cnn PerceptionPerceptual misalignment of texture representations in convolutional neural networks — finds no connection between CNN Brain-Score and alignment with human texture perception, suggesting texture perception involves mechanisms distinct from object recognition CNNs. Based on arXiv:2604.01341.Votes: 0GitHub stars: 3
- System Dse DeepstackDeepStack methodology for design space exploration (DSE) in system-hardware co-design. Scalable and accurate performance modeling for distributed 3D-stacked AI systems. Use when performing early-stage system design optimization, hardware-software co-design, or DSE for AI accelerators. Keywords: DSE, design space exploration, system design, hardware co-design, distributed systems, AI accelerators.Votes: 0GitHub stars: 3
- Structured Sparse Attention EntityStructured-Sparse Attention methodology from arXiv:2605.22476 (May 2026). Blockwise resolvent-style attention operator achieving subquadratic sequence complexity O(n^(4/3)) for entity tracking by exploiting localized attention structure. Use when: efficient attention mechanisms, entity tracking, subquadratic transformers, sparse attention patterns, long-sequence reasoning.Votes: 0GitHub stars: 3
- Structured Search Llm ReasoningMethodology for improving LLM reasoning by making search tree structures explicit in reasoning traces. LinTree (arXiv:2605.31492) shows that adding parent pointers to linearized search traces significantly outperforms implicit reasoning and LLM-heuristic search. Use when optimizing chain-of-thought reasoning, implementing tree search in LLMs, designing reasoning agents, or analyzing search history conditioning in language models. Activation: structured reasoning, search tree LLM, linearized t...Votes: 0GitHub stars: 3
- Staged Training Vlm Perception ReasoningStaged VLM post-training methodology - decomposing VLM capabilities into visual perception, visual reasoning, and textual reasoning stages with specialized data and RL-based perception learningVotes: 0GitHub stars: 3
- Spike Driven Large Language ModelSpike-driven Large Language Model - Spike-based computation for large language models. Activation triggers: spike, driven, large, neuroscience, SNN.Votes: 0GitHub stars: 3
- Sequential Chaotic Oscillations Ei NetworksSequential chaotic oscillations (SCOs) in excitatory-inhibitory threshold-linear networks - dynamical mechanism for sequential metastability in brain dynamics. Activation: sequential metastability, chaotic itinerancy, E-I oscillation, SCO, threshold-linear network, brain dynamics, metastable states.Votes: 0GitHub stars: 3
- Routing Distraction Multimodal MoeRouting analysis and intervention for Multimodal Mixture-of-Experts models. Use when: (1) Debugging vision-language reasoning failures, (2) Analyzing expert routing in MoE architectures, (3) Improving multimodal MoE performance, (4) Understanding cross-modal expert activation. Triggers: mixture-of-experts, MoE routing, multimodal reasoning, vision-language models, expert activation, routing intervention, cross-modal distraction.Votes: 0GitHub stars: 3
- Rim Reasoning Memory Llm Working MemoryRiM (Reasoning in Memory) methodology for unlocking working memory capacity in LLMs via fixed memory blocks, enabling compute-efficient latent reasoning without autoregressive thought generation.Votes: 0GitHub stars: 3
- Rhythm Switching Adaptive Time Constants RnnMethodology for analyzing how recurrent neural networks with neuron-specific adaptive time constants switch between multiple frequency band rhythms. Covers rhythm-switching mechanisms, time constant-frequency relationships, and degeneracy of learned solutions. Activation: rhythm switching RNN, adaptive time constants, frequency band switching, RNN neural dynamics, multi-band rhythms, cortical rhythm mechanisms.Votes: 0GitHub stars: 3
- Rft Visual Continual LearningUsing Reinforcement Fine-Tuning (RFT/GRPO) to overcome catastrophic forgetting in visual continual learning. Activation triggers: reinforcement fine-tuning continual learning, GRPO visual CL, RL visual continual learning, catastrophic forgetting visual, class-incremental visual learningVotes: 0GitHub stars: 3
- Resolvent Rnn Multi Hop SparsityResolvent-RNN (R-RNN) methodology for constraining multi-hop temporal pathways in recurrent neural networks to achieve temporal sparsity alignment. Use when: (1) analyzing or designing RNN architectures with multi-hop temporal dependencies, (2) studying temporal sparsity in sequence modeling, (3) understanding resolvent-based constraints for recurrent dynamics, (4) improving RNN long-range dependency handling, (5) researching spectral methods for RNN stability and expressivity. Triggers: R-RN...Votes: 0GitHub stars: 3
- Relaxation Informed Surrogate TrainingRelaxation-Informed Training (RIT) for neural network surrogate models enabling exact MILP embedding with reduced binary variables. Activation triggers: surrogate optimization, MILP embedding, neural network relaxation, ReLU pruning, optimization surrogateVotes: 0GitHub stars: 3
- Rad 2 Scaling Reinforcement Learning Generator Discriminator FraResearch methodology from paper 'RAD-2: Scaling Reinforcement Learning in a Generator-Discriminator Framework'. arXiv:2604.15308v1. Covers key techniques and approaches for neuroscience research. Activation: rad, 2, scaling, cs.CVVotes: 0GitHub stars: 3
- Poisson Gradient EstimationSystematic comparison of Poisson gradient estimation methods (EAT vs GSM) for latent variable models in computational neuroscience. Activation: poisson gradient, EAT method, Gumbel-SoftMax, spike train inference.Votes: 0GitHub stars: 3
- Neurocybernetic Modeling Large ScaleIntegrative neurocybernetic modeling in the era of large-scale neuroscience. Closed-loop brain-body-environment models, nonlinear state-space, meta-dynamical extensions, knowledge distillation, connectomics-informed architectures. Trigger words: neurocybernetic modeling, closed-loop brain model, brain as controller, state-space neuroscience, large-scale neuroscience integration.Votes: 0GitHub stars: 3
- Neuro Bursty Persistent NetworksBursty Persistent Brain Network (PBN) modeling methodology for neural dynamics with non-Markovian temporal structure. Combines renewal theory, state-dependent intensity functions, and stochastic simulations to model how neuronal avalanches transition between quiescent and active states.Votes: 0GitHub stars: 3
- Network Aware Iv RegressionNetwork-aware Instrumental Variable Regression for Causal Node Discovery and Estimation. Two-stage framework incorporating IVs and graph-fused regularization for sparse causal effects in network-structured exposures with latent confounding. Activation: network IV regression, causal node discovery, graph regularization, brain imaging causal inference.Votes: 0GitHub stars: 3