Data & Analytics

MLE-Toolbox: Comprehensive open-source MATLAB toolbox for end-to-end EEG/MEG analysis with source localization, connectivity analysis, and ML classifiers. Activation: MLE-Toolbox, EEG analysis, MEG analysis, source localization, brain network analysis, neuroimaging toolbox.

Maximum Likelihood Decoding methodology for CSS quantum error correction codes — reformulates MLD as partition function computation in classical spin models, enabling exact MLD via tensor network contraction and approximate MLD via belief propagation. Connects QEC threshold to statistical phase transition. Use when: designing quantum error correction decoders, analyzing CSS code thresholds, computing MLD for surface/toric codes, applying tensor networks to QEC, or studying statistical mechani...

Machine Learning Methods for Studying Latent Neural Activity Dynamics - IJCAI 2026 survey综述机器学习研究神经种群潜伏动力学结构的方法论，涵盖单区域潜伏动力学（LDS/RNN/Neural ODE）、多区域通信、行为对齐建模、神经基础模型（Transformer/扩散模型）

ML-hybrid distributed caching methodology combining traditional caching algorithms (LRU, LFU, ARC, TLRU) with lightweight machine learning for predictive eviction and adaptive sizing. Use when: (1) designing cache systems for dynamic environments, (2) selecting caching strategy based on workload characteristics, (3) implementing ML-enhanced eviction/prefetching layers, (4) optimizing cache performance across distributed architectures, (5) benchmarking caching algorithms across hit ratio, late...

Computational complexity lens for understanding how ML manages complex systems. Based on arxiv:2604.07233 'How Does Machine Learning Manage Complexity?' by Lance Fortnow. Use when analyzing ML's ability to model complex systems, understanding complexity bounds, P/poly-computable distributions, or when asked 'how does ML handle complexity?', 'ML complexity theory', 'computable distributions in ML'.

MIRAGE methodology — robust multi-modal architecture for translating fMRI-to-image models from seen visual decoding to mental imagery reconstruction. Demonstrates that SOTA on seen images doesn't guarantee SOTA on mental imagery and proposes a multi-modal, multi-loss architecture that excels at both. Use when researching: fMRI visual decoding, mental imagery reconstruction, brain decoding generalization, seen-to-imagery transfer, NSD-Imagery dataset, multi-modal brain decoding, vision model g...

Energy-first neural architecture design framework based on biological principles. Systematic validation across vision, text, neuromorphic, and physiological datasets with 2,203 experiments. Activation: energy-first, neural architecture, biological principles, energy-regularized, lambda sweep.

元学习上下文方法实现无需训练的跨被试脑解码。通过上下文学习实现训练无关的跨个体fMRI解码。适用于零样本脑解码、快速脑机接口、个体化神经科学。触发词：元学习脑解码、上下文学习、跨被试、训练无关、零样本。

Meta-Learning Biologically Plausible Plasticity Rules

Measurement-based soft PCA framework using entropy-regularized Fermi-Dirac filters for quantum principal component analysis without eigenvector recovery. Enables dimension-independent sample complexity O(1/eta^2) for fractional-rank scoring. Use when: quantum PCA, soft PCA, Fermi-Dirac filter, measurement-based PCA, quantum data analysis, eigenvector-free PCA, anomaly detection via PCA, spectral energy profiling.

Dynamical mean-field theory for random recurrent networks with low-rank structure and firing-rate-driven adaptation. Identifies four oscillatory regimes: static coherent, noise-sustained oscillations, stochastic switching, global limit cycle. Explains waxing-waning rhythms, Up-Down alternations observed in wakefulness/sleep/anesthesia. Trigger words: mean-field theory, oscillatory dynamics, low-rank recurrent network, Hopf bifurcation, adaptation, neural oscillations, Up-Down states.

Monte Carlo Tree Search (MCTS) methodology for discovering optimal data encoding circuits in quantum-classical neural networks. Addresses the open question of why certain quantum data encodings outperform others by treating encoding circuit design as a sequential decision problem. Use when: quantum data encoding optimization, MCTS quantum circuits, quantum-classical neural network design, QML encoding strategy, quantum feature map discovery.

Multilevel Covariate-Assisted Principal Regression (MCAP) for brain functional connectivity analysis. Handles hierarchically nested neuroimaging data, identifies cluster-specific projections, and models covariance matrix outcomes with subject-level covariates. Use when: analyzing lifespan brain connectivity, multilevel fMRI data, functional connectivity regression, covariance matrix outcomes.

Maximum entropy principle for neural network connectivity — normative framework for understanding how task constraints shape neural connectivity structure without gradient descent.

Majorization lattice supermodularity and subadditivity framework — two structural majorization relations (precursors) underlying supermodularity and subadditivity of all sum-concave functions including Tsallis, Rényi, and Shannon entropies on the majorization lattice. Applies to quantum information theory, entropy inequalities, lattice theory. Activation: majorization, supermodularity, subadditivity, majorization lattice, Tsallis entropy, Rényi entropy, sum-concave, information theory lattice...

Statistical learning theory methodology proving majority-of-three voting is optimal in the realizable PAC setting

Framework for analyzing learning dynamics in low-rank RNNs via overlap space decomposition. Distinguishes loss-visible overlaps (determine activity/output/loss) from loss-invisible overlaps (encode training history). Enables understanding of why functionally equivalent networks learn differently. Activation: low-rank RNN learning, RNN overlap space, loss-visible invisible, RNN gradient descent dynamics, RNN learning theory, Ger Barak RNN.

Local synaptic learning rules (STDP+ and homeostatic plasticity) can implement exact SIGReg-like self-supervised learning gradients without backpropagation, global error signals, or weight transport.

本地基座模型强化学习对齐工程实践 - 涵盖 RLHF/DPO/GRPO 算法选型、显存优化、框架选择、数据工程与全流程实施指南

Identifying structural design principles (local cycles) that shape computational abilities of recurrent neural networks. Found that 2- and 3-cycles strongly enhance computational power, and biologically-inspired interneurons dramatically increase capacity.

Sample complexity analysis methodology for quantum Lindbladian simulation using Wave Matrix Lindbladization (WML) algorithm. Provides explicit non-asymptotic bounds, dimension dependence analysis, and typical-case guarantees for random Lindblad operators. Combines quantum computing with statistical learning theory.

Exact variance and Fano factor analytical formulae for arbitrary level crossings in stationary Gaussian processes. Extends the Kac-Rice mean crossing rate to capture clustering vs. regularity statistics, critical for neuronal spike train analysis, neural coding reliability, and stochastic neural dynamics. Use when analyzing spike train variability, threshold crossing statistics, or neural coding Fano factors.

Learning Dynamic Stability Landscapes in Synchronization Networks methodology - graph-to-image prediction paradigm for predicting stability landscapes from network topology. Pioneers image-like per-node stability landscapes beyond scalar indices. Applicable to neuroscience, power grids, biological synchronization. Activation: stability landscape, synchronization stability, graph-to-image prediction, dynamic stability, oscillator networks, power grid stability.

Comprehensive survey of machine learning methods for studying latent neural activity dynamics - from state-space models to deep generative models covering single-region dynamics, multi-region communication, and neural manifold geometry.