Data & Analytics

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。

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.

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.

Statistical methodology for analyzing O(1) coupling expectations in quantum field theories. Quantifies the spread (ratio of largest to smallest dimensionless couplings) and derives closed-form probability distributions for coupling ratios. Use when: analyzing naturalness in particle physics, studying coupling constant distributions, computing probability bounds for hierarchies in QFT, or applying statistical reasoning to fundamental physics parameters. Activates on keywords: O(1) couplings, c...

CORE (Confounding Robustness Enhancement) framework for out-of-distribution generalization in brain network analysis. Addresses site effects and covariate confounding via causal decoupling. Use when: building cross-site classifiers, dealing with scanner/site bias, handling spurious correlations in neuroimaging data, conducting multi-center studies, applying graph neural networks to brain connectivity with domain shifts.

DBNs spontaneously organize representations by class without supervision.

Separating wiring-specific from statistical control of dynamics in a complete connectome. Analysis of larval Drosophila brain showing coarse statistics set dynamical regime while specific wiring determines activity routing.

Connectome-Constrained Neural Network (CCNN) methodology for brain-inspired AI. Integrates biological structural connectivity (connectome) into artificial neural network architectures to improve generalization and biological plausibility. Activation: connectome constraint, structural connectivity, brain-inspired architecture, connectome-based AI, wiring cost, brain network prior, diffusion MRI connectivity.

Congestion-Aware Dynamic Axonal Delay mechanism for Spiking Neural Networks. Decomposes delay into channel-wise static base delay + global activity-conditioned shift. Reduces delay parameters by ~50% while improving accuracy on temporal tasks. Source: arXiv:2605.01291 (Bai et al., May 2026).

Congestion-Aware Dynamic Axonal Delay for Spiking Neural Networks. Replaces static per-synapse delays with input-dependent dynamic delays that adapt to network activity patterns, reducing delay parameters while improving temporal task performance. Activation: congestion-aware delay, dynamic axonal delay SNN, input-dependent delay, SNN temporal processing, adaptive delay learning.

早停策略技能 - 利用中间答案的置信度动态来决定何时终止推理，适用于大推理模型的长链式思维生成。基于论文 Early Stopping for Large Reasoning Models via Confidence Dynamics (arXiv 2604.04930)。激活关键词: 早停, early stop, confidence dynamics, reasoning stop, 推理终止, overthinking prevention, 防止过度思考。

Cognition-Inspired Dual-Stream Semantic Enhancement (DuSE) for Vision-Based Dynamic Emotion Modeling. Implements hierarchical temporal prompt clusters (HTPC) for cognitive priming and latent semantic emotion aggregators (LSEA) for knowledge integration. Models neuro-cognitive mechanisms from Conceptual Act Theory for dynamic facial expression recognition. Use for: emotion recognition, cognitive-inspired computer vision, neuro-cognitive modeling, dynamic facial expression analysis.

Hybrid CNN-SNN architecture for EEG-based imagined speech decoding. First integration of spiking neural networks into imagined speech BCI, achieving 80.13% accuracy on BCI Competition III benchmark. Activation: imagined speech, EEG decoding, CNN-SNN hybrid, spike-based BCI, neuromorphic BCI

Scalable neuromorphic computing via autonomous spiking dynamics in clockless (asynchronous) digital circuits implemented on FPGAs. Boolean spiking neurons with configurable excitatory/inhibitory weights, spike-encoded data processing pipeline. Bridges gap to analog neuromorphic systems without specialized hardware. Based on Oliveira Gomes & Rontani (arXiv: 2605.16114). Use when designing energy-efficient neuromorphic systems on FPGAs, exploring clockless asynchronous digital circuits for neur...

Circuit-level spiking neuron model for hardware robustness analysis. Studies how transistor-level variations affect SNN reliability on neuromorphic chips. Activation: circuit-level SNN, neuromorphic hardware reliability, transistor variation spiking, hardware spiking neuron, CMOS spiking, SNN fault tolerance

CFSPMNet - Cross-subject Fourier-guided Spatial-Patch Mamba Network for EEG Motor Imagery Decoding in Stroke Patients. Use when working with MI-EEG decoding, cross-subject BCI for stroke rehabilitation, Mamba-based EEG models, or Fourier-domain token reorganization for neural decoding.

Convolutional Neural Network framework for detecting gaseous microemboli (GME) during cardiac procedures using transthoracic ultrasound. Activation triggers: emboli detection, cardiac ultrasound, microemboli GME, surgical safety, transcatheter monitoring

CaMBRAIN methodology for real-time continuous EEG inference using causal Mamba state space models. First model enabling long-range streaming inference of variable-length EEG signals with >10x higher throughput.

**arXiv ID:** 2303.13651 **Authors:** Jascha Achterberg, Danyal Akarca, Moataz Assem, Moritz Heimbach, Duncan E. Astle, John Duncan **Published:** 2023-03-21T18:36:17Z **Abstract:** There is a concerted effort to build domain-general artificial intelligence in the form of universal neural network models with sufficient computational flexibility to solve a wide variety of cognitive tasks but without requiring fine-tuning on individual problem spaces and domains. To do this, models need appropr...

Lightweight self-supervised representation learning for fMRI using positive-only data pairs, achieving strong cross-task generalization without large-scale pretraining

BrainDyn: A Sheaf Neural ODE framework for modeling continuous-time brain dynamics on structured graphs. Combines LSTM stalks with sheaf Laplacian message passing and neural ODE evolution. Apply when: brain dynamics modeling, fMRI/EEG forecasting, generative brain models, neural ODEs, sheaf theory, brain graph networks, perturbation prediction, synthetic brain data. Keywords: sheaf neural ODE, brain dynamics, fMRI modeling, EEG forecasting, brain graphs, sheaf Laplacian, neural ODE, brain reg...

BrainCast methodology for spatio-temporal forecasting of whole-brain fMRI time series. Uses dual-branch architecture with ST-CausalConv for spatial decoding and ST-Mixer for temporal prediction. Activation: fMRI forecasting, brain time series prediction, spatio-temporal brain modeling.

Brain-LLM alignment is driven by training-language dominance, not an inherent property of English. Tests with fMRI from 112 participants across English, Chinese, French and 7 LLMs (English-dominant, Chinese-dominant, multilingual). Baichuan2-7B reverses alignment gradient entirely; typological distance independently affects alignment degradation in syntax regions (IFG). Accepted at CoNLL 2026. Activation: brain-LLM alignment, cross-linguistic brain encoding, training data dominance, multiling...

Graph Neural Network methods for brain connectivity analysis. Use when analyzing fMRI/EEG brain network data, modeling brain structure-function relationships, predicting cognitive outcomes from connectome data, or applying GNN to neuroscience problems. Keywords: brain graph, connectome GNN, neural network brain, fMRI GNN, brain connectivity analysis, 脑网络图神经网络, 脑连接性分析, 认知预测.