Data & Analytics

EEG-based Hopfield energy landscape analysis for quantifying brain network stability during emotional processing (happy/sad face tasks). Activation: emotion energy landscape, brain stability, happy sad face EEG, Hopfield emotion.

EEG-conditioned framework for reconstructing dynamic fMRI as continuous neural sequences with high spatial fidelity and temporal coherence at cortical-vertex level. Incorporates null-space intermediate-frame reconstruction for handling sampling irregularities.

Dynamical quantum optimal transport (QOT) methodology based on Benamou-Brenier formulation for computing geodesics between positive semidefinite matrices. Use when: computing quantum state transport distances, solving quantum chemistry problems via optimal transport, analyzing numerical convergence of QOT distances, or implementing interior-point methods for quantum density matrix geodesics. Activation: quantum optimal transport, dynamical QOT, Benamou-Brenier, quantum chemistry optimal trans...

Gaussian-equivalent process methodology for analyzing nonlinear noise in recurrent neural circuits using Ornstein-Uhlenbeck noise matching and lognormal moment closure. Activation: mean field, nonlinear noise, recurrent networks, OU process.

Dynamic Gated Neuron (DGN) - Biologically plausible gating mechanism for Spiking Neural Networks via dynamic membrane conductance modulation. Enables selective input filtering and adaptive noise suppression. Activation triggers: dynamic gated neuron, DGN, SNN gating, conductance-based SNN, robust spiking neural network, biological gating.

DRIADA Python toolkit for cross-scale analysis of single-neuron selectivity and population dynamics. Unifies neural signals (calcium imaging, spike trains, simulated networks) with time-aligned behavior in a shared data model for selectivity testing, dimensionality reduction, and network analysis.

Statistical-mechanical theory of dreaming in memories.

Trainability-by-Design methodology for scalable Quantum Machine Learning using Dynamical Lie Algebra (DLA) constraints. Embeds group-theoretic geometric priors as structural regularizers to restrict DLA growth to polynomial regime, guaranteeing gradient-rich training landscapes while avoiding barren plateaus. arXiv:2606.31536

Distribution-valued brain connectivity analysis using vector quantiles instead of scalar edge weights. Based on Mhanna, Achard, Petersen (2026, HAL). Use when building brain connectivity graphs from fMRI/EEG, improving connectome classification, or representing higher-order connectivity statistics. Activation: distribution-valued brain connectivity, graph brain representation, fMRI connectome classification, voxel clustering, brain network edges, vector quantile connectivity.

**arXiv ID:** 2509.16215 **Authors:** Izavan dos S. Correia, Henrique C. T. Santos, Tiago A. E. Ferreira **Published:** 2025-09-05T15:32:23Z **Abstract:** This study proposes a deep learning-based approach for discovering loops in programming code according to their potential for parallelization. Two genetic algorithm-based code generators were developed to produce two distinct types of code: (i) independent loops, which are parallelizable, and (ii) ambiguous loops, whose dependencies are unc...

Discounted Model Predictive Control (MPC) and infinite-horizon optimal control under plant-model mismatch. Unified framework for stability and suboptimality analysis with robustness guarantees. Use for: robust MPC, plant-model mismatch handling, discounted optimal control, stability analysis, surrogate model control. Activation: discounted MPC, plant-model mismatch, robust MPC, infinite-horizon control, suboptimality analysis.

DINA (Dual-Tower Image-Neural Alignment) framework for interpretable contrastive analysis of V1 population activity. Aligns visual stimuli and V1 responses in shared latent space at intermediate feature map level. Activation: DINA, V1 population activity, image-neural alignment, contrastive framework, calcium imaging decoding, visual computation.

Digital quantum reservoir computing (QRC) framework for time series forecasting on near-term quantum devices. Uses parametrized four-qubit reservoirs with partial measurement and reset, encoding temporal data in rotation angles. Training restricted to classical Ridge-regression readout. Use when: quantum reservoir computing, time series forecasting, near-term quantum devices, ATM cash demand prediction, quantum ML for financial data.

**arXiv ID:** 2412.00104 **Authors:** Alex Nguyen, Gautam Reddy **Published:** 2024-11-27T22:12:29Z **Abstract:** Transformers exhibit in-context learning (ICL): the ability to use novel information presented in the context without additional weight updates. Recent work shows that ICL emerges when models are trained on a sufficiently diverse set of tasks and the transition from memorization to generalization is sharp with increasing task diversity. One interpretation is that a network's limit...

Methodology for auditing and distilling deep reinforcement learning hedging policies into interpretable symbolic formulas. Includes framework for analyzing delta corrections relative to Black-Scholes, symbolic regression distillation, and regime fragility stress-testing. Use when analyzing neural hedging strategies, quantitative risk management, options hedging with RL, or making black-box financial AI auditable.

深度连续局部学习方法论（DECOLLE）。在脉冲神经网络中实现局部突触可塑性规则，通过合成梯度实现端到端训练。适用于事件驱动视觉、神经形态计算、在线学习、脉冲神经网络研究。触发词：DECOLLE、脉冲神经网络、突触可塑性、局部学习、神经形态计算、在线学习、spiking neural network、synaptic plasticity、neuromorphic computing。

**arXiv ID:** 1911.05479 **Authors:** Asim Iqbal, Phil Dong, Christopher M Kim, Heeun Jang **Published:** 2019-10-26T05:02:33Z **Abstract:** Finding a code to unravel the population of neural responses that leads to a distinct animal behavior has been a long-standing question in the field of neuroscience. With the recent advances in machine learning, it is shown that the hierarchically Deep Neural Networks (DNNs) perform optimally in decoding unique features out of complex datasets. In this s...

Decentralized Momentum Tracking with Biased Gradients (Biased-DMT) for large-scale distributed optimization. Handles communication compression and data heterogeneity in decentralized learning. Use for: decentralized optimization, federated learning, distributed ML, gradient compression, biased gradients. Activation: decentralized optimization, biased gradients, momentum tracking, distributed learning, federated learning, gradient compression.

**arXiv ID:** 2211.02658 **Authors:** Omid Gheibi, Danny Weyns **Published:** 2022-11-04T07:45:48Z **Abstract:** Recently, machine learning (ML) has become a popular approach to support self-adaptation. ML has been used to deal with several problems in self-adaptation, such as maintaining an up-to-date runtime model under uncertainty and scalable decision-making. Yet, exploiting ML comes with inherent challenges. In this paper, we focus on a particularly important challenge for learning-based...

Neurodegenerative diagnosis via multimodal fusion.

Data-driven methodology for selecting optimal subsampling rates in SDE parameter estimation when data-model compatibility scales are unknown.

Continuous-variable quantum neural networks (CV-QCNN) for biomedical image classification methodology. Uses photonic circuit simulation with Gaussian gates (displacement, squeezing, rotation, beamsplitters) to emulate convolutional behavior for medical imaging tasks. Activation: continuous variable quantum, CV quantum neural network, photonic quantum imaging, biomedical image classification, CV-QCNN, MedMNIST quantum, quantum medical imaging, photonic circuit simulation, Gaussian gate convolu...

Cross-scale spatially-aware generative modeling for transcriptomic programs underlying neurodegenerative brain organization. Variational framework linking gene expression to cortical degeneration with graph-based spatial smoothness. Activation: spatially-aware generative, transcriptomic neurodegeneration, cross-scale brain modeling, cortical thinning prediction, gene-expression degeneration.

Measuring cross-modal neural network convergence using single-stimulus intra-modal dispersion. Generalized Procrustes Algorithm for quantifying how stimuli with low intra-modal dispersion elicit higher cross-modal alignment. Activation triggers: cross-modal convergence, neural network alignment, vision-language alignment, representational similarity.