Data & Analytics

Analytical solution for large nonlinear recurrent neural networks at fixed connectivity. Calculates moments and response functions without synaptic weight averaging, linking connectivity to spontaneous activity and perturbation response. Trigger words: nonlinear RNN, fixed connectivity, moments, response functions, large N limit.

Nonequilibrium physics framework for brain dynamics analysis. Covers entropy production, time-irreversibility, broken detailed balance, and nonequilibrium computation in neural systems. Use when analyzing brain dynamics from nonequilibrium statistical physics perspective, measuring entropy production, studying time-irreversibility in neural data, or investigating consciousness/cognitive complexity through nonequilibrium metrics.

Comprehensive review of nonequilibrium physics in neuroscience. Analyzes time-irreversibility, entropy production, and broken detailed balance in neural dynamics as signatures of cognitive complexity and consciousness.

Information geometry framework for analyzing non-Euclidean structure of visual space — modeling perceptual geometry using Riemannian manifolds, Fisher information, and Finsler geometry. Activation: visual space, non-Euclidean, information geometry, Riemannian manifold, perceptual geometry, Fisher information, visual perception, psychophysics.

NeuroSTORM - Neuroimaging Foundation Model with Spatial-Temporal Optimized Representation for fMRI analysis. Trained on 28.65M frames from 50,000 subjects using shifted scanning Mamba backbone. Activation triggers: fMRI foundation model, neuroimaging, NeuroSTORM, brain analysis, Mamba fMRI, spatial-temporal modeling.

CNN + Adversarial Autoencoder (AAE) for EEG signal classification — from raw EEG to image representations, latent-space regularization, and robust brain-computer interface (BCI) decoding.

Skill for exploring brain networks using noninvasive electrophysiological measurements (EEG/MEG) based on arXiv:2607.17602v1.

Neural Variational Quantum Linear Solver (NVQLS) - first hybrid quantum-classical operator learning framework using Legendre-Galerkin weak formulation for solving parametric PDEs. Achieves superior accuracy with theoretical computational complexity advantages under efficient state preparation. Activation: quantum operator learning, quantum PDE solver, variational quantum linear solver, VQLS, quantum spectral method, quantum Galerkin method.

Neural operator framework for data-driven discovery of stability and receptivity properties in physical systems. Activation: neural operator, stability discovery, receptivity analysis, dynamical systems.

Theory of learning high-dimensional controlled non-linear dynamical systems via neural ODEs trained with online stochastic gradient descent, solved using dynamical mean field theory. Activation: neural ode, mean field theory, dynamical systems, training dynamics, learning curves, high-dimensional limit, statistical mechanics, online SGD, ResNet theory.

Neural network quantum state (NQS) architecture for grand canonical ensemble bosonic systems. Enables variational Monte Carlo with variable particle number in Fock space. Activation: neural quantum states, grand canonical ensemble, bosonic wavefunctions, Fock space, variational Monte Carlo, NQS, quantum many-body ground state.

Unified Rosetta Stone framework for neural mass models. Provides mathematical tools connecting different neural mass model formulations for brain dynamics analysis across scales from single-neuron spiking to macroscopic fMRI/MEG/EEG. Applies to: brain dynamics modeling, neural mass models, computational neuroscience, multi-scale brain modeling. Activation: neural mass models, rosetta stone neural, brain dynamics tools, neural mass unified, computational brain modeling.

Neural 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. Activation triggers: neural manifold, latent dynamics, population activity, dimensionality reduction, neural state space, behavior decoding.

Deep learning-based decoders for quantum error correction (QEC) that outperform traditional algorithms (MWPM, belief propagation) in speed and adaptability to realistic noise models.

Theory of critical dynamics and information processing in neural networks. Neural systems at critical points exhibit optimal information processing, maximal dynamic range, and power-law distributed avalanches. Provides methods for identifying, analyzing, and exploiting critical regimes in both biological and artificial neural networks. Applicable to critical brain hypothesis, neural avalanche analysis, optimal computation regimes. Trigger: neural criticality, critical dynamics, neural avalanc...

脑连接矩阵交互式可视化工具。基于HTML5/JavaScript的浏览器端应用，支持EEG、ECoG、MEG、fMRI等高维神经连接数据的3D堆叠矩阵可视化，实时交互探索连接模式。适用于脑连接分析、神经数据可视化、连接组学。触发词：脑连接可视化、连接矩阵、神经网络可视化、connectivity matrix、brain connectivity visualization、EEG connectivity、MEG connectivity。

神经编码动力学分析框架 - 整合计算神经科学、机器学习和临界态理论，研究生物与人工神经网络编码表示动力学。涵盖临界脑假说、雪崩动力学、信息几何与动力学不变量。Activation: neural coding, dynamics analysis, critical brain hypothesis, avalanche dynamics, information geometry, dynamical invariants, neural representation, encoding dynamics, computational neuroscience.

Multi-view Information Bottleneck framework for modeling higher-order interactions (HOIs) in resting-state fMRI for psychiatric diagnosis. Captures complex brain dynamics beyond pairwise connectivity without predefined hyperedges.

Multisensory learning methodology that recruits visual neurons into olfactory memory engrams through cross-modal binding. Using Drosophila model to study how combining sensory modalities expands memory engrams and improves recall performance. Activation triggers: multisensory learning, memory engram, cross-modal binding, neural circuits, sensory integration.

Multi-view O-Information framework for modeling higher-order brain interactions (HOIs) in fMRI data. Information-theoretic approach to psychiatric diagnosis using triadic and tetradic brain connectivity patterns. Keywords: O-information, higher-order interactions, fMRI analysis, information bottleneck, psychiatric diagnosis, hypergraph

Multi-scale information geometry framework revealing the structure of mutual information in neural populations. A unique Riemannian representational geometry emerges from coarse-graining, extending Fisher information metric to capture encoding structure from fine to coarse stimulus distinctions. Use when researching neural population coding, information geometry, Fisher information in neuroscience, or neural representational geometry. Based on arXiv:2605.06304.

MOJO (Masked autOencoder-based JOint training) framework for leveraging unlabelled neural data via self-supervised learning combined with supervised objectives. Enables robust neural population decoding with limited labelled data across species and modalities.

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.

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）