Data & Analytics

Methodology for extracting high-order functional brain network structures beyond pairwise connections under global constraints. Addresses theoretical limitations of pairwise FBN modeling. Activation: higher-order brain networks, beyond pairwise, global constraints, FBN limitations.

Higher-order brain network analysis using topological signal processing. Captures circulatory and multi-node interactions beyond pairwise graph models.. Activation: higher-order networks, topological signal processing, brain connectomics.

Parallelized Hierarchical Connectome (PHC) framework that upgrades temporal State-Space Models into spatiotemporal recurrent networks for brain connectivity modeling.. Activation: hierarchical connectome, state-space models, spatiotemporal.

Robust volatility updates for Hierarchical Gaussian Filtering (HGF). Improves stability and convergence of uncertainty estimation in perceptual inference. Activation: hierarchical gaussian filter, volatility update, perceptual inference, active inference, uncertainty estimation.

Hardware-aware open-source framework for mixed-signal Spiking Neural Network design space exploration. Captures non-ideal analog/digital hardware behavior while supporting system-level exploration for energy-efficient neuromorphic edge computing. Activation: mixed-signal SNN, hardware-aware simulation, design space exploration, neuromorphic edge, non-ideal hardware modeling, SNN accelerator

Hamming quantum kernel for SVMs that avoids exponential concentration problem of fidelity quantum kernel. Uses full measurement statistics rather than single fidelity value. Outperforms fidelity kernel at 15+ qubits and classical Gaussian kernel on synthetic quantum data. Scales to 27 qubits without additional quantum resources. Activation: hamming quantum kernel, quantum SVM, exponential concentration, quantum kernel scalability, fidelity kernel alternative, scalable quantum kernel

Extremely slow scaling of minimal Hamming distance in quantum sampling data. Use when analyzing quantum algorithms, complexity bounds, quantum ML architectures, or quantum error correction involving mathematical analysis and statistical methods.

Hamiltonian-encoded quantum reservoir computing methodology for robust quantum learning on NISQ platforms. Addresses trainability (barren plateaus), hardware efficiency, and information stability through direct Hamiltonian mapping and quantum dynamical evolution.

GTaS Generative Spike Train Model

Methodology for analyzing and mitigating grokking, epoch-wise double descent, and late-stage generalization decay in overparameterized quantum neural networks via weight-norm regularization.

Inter-areal predictive coding for gradient-free continual learning in spiking neural networks. Brain-inspired learning rule using feedback connections to transmit prediction errors without backpropagation. Keywords: gradient-free learning, continual learning, predictive coding, inter-areal, SNN, catastrophic forgetting, bio-inspired.

Geometric Stability of Neural Population Codes methodology - Shesha metric quantifying pairwise distance structure reproducibility across split-half RDMs, dissociable from temporal stability and decoding accuracy. Use when analyzing representational reliability beyond centroid drift, comparing brain regions, or modeling attractor-network mechanisms for RDM consistency. Activation: geometric stability, Shesha, split-half RDM, representational dissimilarity, neural population code, striatum hip...

Geometric Quantum Physics-Informed Neural Network (GQPINN) methodology for solving PDEs with symmetry-aware quantum circuits. Combines geometric quantum machine learning with physics-informed neural networks. Use when solving PDEs with quantum circuits, incorporating symmetry/inductive biases into quantum models, or designing equivariant quantum ansatzes for scientific ML. Activation: geometric quantum, symmetry-aware PINN, quantum PDE solver, equivariant quantum circuit, GQPINN, quantum phys...

Geometric origin of exact mean-field reductions using Möbius symmetry and the Lorentzian Ansatz — proving the Cauchy-Lorentz family uniquely emerges as invariant under projective transport, unifying Ott-Antonsen and Montbrió-Pazó-Roxin reductions.

Geometric Basis Functions (GBF) framework for noninvasive whole human brain dynamics mapping using participant-specific eigenmodes derived from cortical geometry. Use when working with EEG/MEG source imaging, brain dynamics reconstruction, neuroimaging inverse problems, or cortical geometry-based neural activity mapping. Enables high-fidelity spatiotemporal reconstruction of neural sources using geometric constraints.

Geometric Basis Functions (GBF) methodology for noninvasive whole human brain dynamics mapping using participant-specific cortical eigenmodes. Reconstructs whole-brain spatiotemporal dynamics from EEG/MEG with anatomically-constrained source imaging. Activation - geometric basis functions, GBF, brain dynamics, source imaging, cortical geometry, EEG/MEG reconstruction.

Genetic and environmental architecture of human functional connectome using extended twin modeling. Separates measurement error from non-shared environment to estimate true connectivity heritability. Keywords: functional connectome, twin modeling, heritability, genetic architecture, brain connectivity.

Functional ensembles as units of computation in deep spiking networks. First-order functionally-connected (1FC) groups based on pairwise correlations, aggregate cofiring predicts downstream responses, ReLU-like input-output relationship with ensemble-size scaling, rare high-coordination events encode information. Activation: functional ensemble, SNN computation, functional connectivity, 1FC group, ensemble cofiring, deep spiking network analysis.

Functional Ensembles as Units of Computation in Deep Spiking Networks. 1FC (first-order functionally-connected) ensembles framework for analyzing information encoding in SNNs through rare coordinated firing events.

Statistical benchmarking methodology for EEG motor-imagery BCI decoders using Friedman-Nemenyi tests. Proves no single decoding pipeline dominates across subjects — personalized model selection adds ~7% accuracy over best fixed choice. Use when evaluating BCI decoders, comparing multi-classifier performance, or designing subject-aware BCI systems.

Foundation models for discovering robust biomarkers of neurological disorders from dynamic functional connectivity. Use when: building neurological biomarker discovery pipelines, applying foundation models to fMRI/EEG data, analyzing dynamic functional connectivity for disease detection, developing robust cross-subject biomarkers. Triggers: brain biomarker foundation model, dynamic functional connectivity biomarker, neurological disorder detection, robust biomarker discovery, fMRI foundation ...

Novel approach to dictionary learning on fMRI data that explicitly accounts for individual brain geometry variability using optimal transport (Fused Gromov-Wasserstein distance) with amortized optimization for computational efficiency.

Tail-certified quantum metrology for quenched sensors — Fisher-zero integrability transition, no-go theorem on averaged Fisher data, universal design laws (safe windows, nondegenerate portfolios, Fisher reserves, Fisher-cut criteria). Activation: quantum metrology, Fisher information, quenched environments, tail certification, NV centers, superconducting qubits, Fisher glass, QFI certification

First-in-human quantum entanglement imaging methodology using J-PET plastic scintillator scanner. Measures polarization correlations of annihilation photons from positron-electron annihilation in vivo for clinical diagnostics. Use when: quantum PET imaging, entanglement-based medical imaging, J-PET scanner design, polarization-correlated tomography, quantum entanglement degree as biomarker, 68Ga radiopharmaceutical quantum imaging.