All categories
Research
Research, evidence gathering, literature, reports, investigation, and synthesis
- 21,402
- 892
Security grades appear on each card once the skill has been scanned. Newly imported skills may briefly show without a grade until the backfill job runs.
Open in full browserBrowse research skills
Showing 11,809–11,832 of 21,402 skills
- Cross Species Rsa Brain AlignmentCross-Species RSA methodology for comparing brain-DNN alignment across human fMRI and macaque electrophysiology. Tests five learning rules (BP, FA, PC, STDP, untrained) across species showing conserved early visual alignment but divergent higher-area rankings. Use when: comparing species in brain encoding models, evaluating learning rule biological plausibility, cross-species validation of brain-DNN alignment, RSA with electrophysiology data. Triggered by: cross-species RSA, brain-DNN alignme...Votes: 0GitHub stars: 3
- Cross Lingual Llm Brain AlignmentMulti-lingual whole-brain encoding framework examining brain-LLM alignment across three typologically distinct languages (Mandarin, English, French). Shows that transformer-based models predict activity in widely distributed cortical functional networks (limbic, ventral attention, default mode, subcortical) across languages, revealing computational roots of cross-linguistic neural alignment with LLM representations. Activation: cross-lingual brain alignment, multilingual fMRI encoding, LLM-br...Votes: 0GitHub stars: 3
- Criticality Constrained Snn PruningCriticality-Constrained Quadratic Pruning (CQP) methodology for energy-efficient SNN deployment on neuromorphic hardware. Combines weight magnitude with surrogate-gradient criticality into analytically exact importance metric. Identifies continuous-relaxation trap, zombie-weight failure mode, and criticality cliff phenomenon. Achieves 95.6% accuracy at 90% sparsity on MNIST; 73% energy reduction at 70% sparsity.Votes: 0GitHub stars: 3
- Critical Flicker Fusion Plasticity BoundaryFramework for using Critical Flicker Fusion Frequency (CFFF) as a falsifiable boundary between plastic and non-plastic neural systems, with explicit operational criteria and hierarchical analysis.Votes: 0GitHub stars: 3
- Crisp Rl Clifford VqaCRiSP — Reinforcement learning with Neural-Guided MCTS for Clifford circuit initialization of Variational Quantum Algorithms. Uses Transformer-based policy trained via self-play to insert learned Clifford gates before fixed parameterized rotations, enabling high-quality VQA initialization through polynomial-time classical stabilizer simulation.Votes: 0GitHub stars: 3
- Cqp Criticality Constrained Snn PruningCriticality-Constrained Quadratic Pruning (CQP) for energy-efficient SNNs combining weight magnitude with surrogate-gradient criticalityVotes: 0GitHub stars: 3
- Coset Ensemble Decoder QecCoset Ensemble Decoder for Quantum Error Correction with Algorithm-Hardware Co-Design methodology. Ensemble forest exploration exploiting logically equivalent cosets to improve Union-Find decoding, with domain-specific FPGA architecture reducing LUT consumption 8.2x. Use when: (1) designing QEC decoders for fault-tolerant quantum computing, (2) optimizing accuracy-latency trade-offs in real-time syndrome decoding, (3) implementing hardware-efficient QEC decoders on FPGA, (4) ensemble decoding...Votes: 0GitHub stars: 3
- Cortiva Candidate Score FusionCORTIVA methodology for candidate-score fusion of complementary visual teachers for EEG- and MEG-to-image retrievalVotes: 0GitHub stars: 3
- Cortical Microcircuit Information FluxSimulation-based reverse engineering methodology for analyzing whether cortical microcircuits are structurally organized to optimize information flux. Covers information flux quantification via mutual information, Recurrence Resonance mechanisms, core-embedding network architecture analysis, and bias-fluctuation contributions to neural dynamics. Applicable to: (1) biological neural circuit functional interpretation, (2) artificial recurrent system design including reservoir computers, (3) cor...Votes: 0GitHub stars: 3
- Cortical Geometry Wiring Rnn Inductive BiasHarnessing cortical geometry, wiring, and function as inductive biases for recurrent neural networks — biologically grounded RNNs using MICrONS connectomics data (spatial coordinates, anatomical connectivity, functional relationships) to achieve superior learning performance.Votes: 0GitHub stars: 3
- Corteg Eeg Ecog Cross ModalityCORTEG: Cross-modality transfer framework that adapts pretrained scalp-EEG foundation models to intracranial ECoG recordings. Combines EEG FM backbone with electrode-aware KNNSoftFourier spatial adapter, dual-stream tokenizer (low-frequency + high-gamma), and leave-one-subject-out fine-tuning. Enables competitive ECoG decoding with only 10-30 minutes of calibration data per patient. Activation: CORTEG, EEG foundation model, ECoG decoding, cross-modality transfer, scalp-to-intracranial, brain-...Votes: 0GitHub stars: 3
- Coral Open Ended DiscoveryAutonomous multi-agent open-ended discovery workflow inspired by the CORAL paper. Use when the task requires sustained search, iterative improvement, multi-agent exploration, shared knowledge accumulation, asynchronous parallel attempts, periodic reflection, or heartbeat-style redirection. Best for research discovery, algorithm design, systems optimization, skill discovery, long-running coding/search tasks, and open-ended problems where a single linear attempt is likely to plateau.Votes: 0GitHub stars: 3
- Convex TokenizationConvexTok methodology — formulating tokenizer construction as a convex optimization (linear program) instead of greedy BPE/Unigram. Use when: (1) Designing tokenizers for new languages or domains, (2) Improving bits-per-byte (BpB) efficiency of LLM tokenizers, (3) Evaluating tokenizer quality beyond greedy heuristics, (4) Tokenizer research comparing BPE/Unigram vs. globally optimal approaches.Votes: 0GitHub stars: 3
- Tunneling Phase Diagram MlTunneling phase diagram methodology — machine learning framework for decoupling true quantum tunneling strength from composite kinetic isotope effects. For quantum chemistry, ML-driven quantum analysis, and kinetic modeling.Votes: 0GitHub stars: 3
- Stp Stabilizes Goal Conditioned Dynamics短时程突触可塑性(STP)稳定目标条件化动力学方法论。研究STP如何在PFC储水池模型中支持多步目标导向行动规划，通过动态调节有效连接保持目标信息。Activation: STP, goal-conditioned dynamics, PFC reservoir, action planning, 突触可塑性, 目标导向行为.Votes: 0GitHub stars: 3
- Parametrically Driven Oscillator NeuromorphicReservoir computing using parametrically-driven oscillators and frequency combs (arXiv:2604.21861). Demonstrates neuromorphic computation via two-mode parametric oscillator with 2:1 resonance across sub-threshold, parametric resonance, and frequency-comb regimes. Covers drive amplitude input encoding, temporal/spectral response sampling, chaotic time-series prediction (Mackey-Glass, Rossler, Lorenz), and design principles for tuning physical oscillator-based reservoir computers.Votes: 0GitHub stars: 3
- Ode Complexity DynamicsComplexity theory analysis of Ordinary Differential Equations. Examines computational complexity of ODE solutions, existence conditions, and complexity barriers. Trigger words: ODE complexity, computational complexity differential equations, ODE existence conditions, complexity barriers, numerical analysis complexity.Votes: 0GitHub stars: 3
- Noise Induced Group Level Synchronization OscillatorsCommon noise-induced synchronization methodology for uncoupled oscillator groups. Demonstrates that groups receiving the same common noise synchronize at the collective level without inter-group coupling.Votes: 0GitHub stars: 3
- Neuromorphic Oscillator Reservoir ComputingReservoir computing using parametrically-driven oscillators and frequency combs for neuromorphic computation. Three-regime system (sub-threshold, parametric resonance, frequency-comb) with 2:1 resonance. Optimal performance at parametric resonance boundary. Applications: chaotic time-series prediction (Mackey-Glass, Rössler, Lorenz), edge AI, analog neural networks.Votes: 0GitHub stars: 3
- Neuromechanical Locomotion DynamicsNeuromechanical modeling framework that connects neural activity to behavioral locomotion dynamics. Combines spectral mode representations with Helmholtz-Nambu decompositions and Bayesian inference to infer predictive stochastic models from neural population data. Activation: neuromechanics, locomotion dynamics, neural-behavior mapping, Helmholtz-Nambu, C. elegans, optogenetic control, behavior prediction from neural activity.Votes: 0GitHub stars: 3
- Narx Topological Phase MappingNARX neural network methodology for deterministic mapping of topological phase transitions in quantum systems. Uses autoregressive exogenous inputs to discover functional identities between topological invariants and critical parameters.Votes: 0GitHub stars: 3
- Kuramoto Oscillatory Phase EncodingKuramoto Oscillatory Phase Encoding for Vision Transformers - neuro-inspired synchronization-based phase encoding that mimics biological oscillatory neural dynamics. Uses Kuramoto model to encode spatial information through phase relationships for efficient vision transformers. Activation: kuramoto phase encoding, oscillatory encoding, vision transformer phase, biological synchronization, neural oscillator encoding.Votes: 0GitHub stars: 3
- Fast Efficient Coding Gain AdaptiveFast efficient coding and sensory adaptation in gain-adaptive recurrent networks — unified mechanistic model reconciling adapter-repulsion and prior-attraction phenomena via gain modulation.Votes: 0GitHub stars: 3
- Fade Adaptive Weight DecayFADE: Forgetting through Adaptive Decay for continual learning. Adapts per-parameter weight decay rates online via meta-gradient descent. Balances acquiring new knowledge with retaining old. Activation: FADE, adaptive weight decay, continual learning forgetting, meta-gradient weight decay, controlled forgetting, Ramesh Schmidhuber.Votes: 0GitHub stars: 3