Data & Analytics

Skill for understanding and applying the mechanistic model of perceptual inference in visual cortex equivalent to a minimal diffusion model (arXiv:2607.15693). Enables extraction of principles linking sparse coding, recurrent dynamics, and diffusion model training for neuroscience-inspired machine learning.

Variational framework for statistical inference on cyclic interactions in directed networks. Directed interactions as edge flows on simplicial complex evolved under energy-minimizing dynamics, yielding low-dimensional cycle space for recurrent organization. Activation: cyclic interaction, harmonic flow, cycle space, simplicial complex, recurrent network, directed graph cycles.

**arXiv ID:** 2302.00789 **Authors:** Yuan Yue, Jeremiah D. Deng, Dirk De Ridder, Patrick Manning, Divya Adhia **Published:** 2023-02-01T22:48:45Z **Abstract:** Obesity is a common issue in modern societies today that can lead to various diseases and significantly reduced quality of life. Currently, research has been conducted to investigate resting state EEG (electroencephalogram) signals with an aim to identify possible neurological characteristics associated with obesity. In this study, we...

脑基础模型方差分配问题方法论。揭示BFMs预测认知失败的根本原因 - 预训练捕获主要方差成分但丢失三阶统计量（协偏度）。线性协偏度子空间FC方法超越所有BFMs（无预训练、无GPU）。规模悖论：BrainLM 650M预测认知比111M更差。适用于脑基础模型评估、认知预测、fMRI分析、统计量保留。触发词：brain foundation models、BFM、variance allocation、third-order statistics、co-skewness、cognition prediction、脑基础模型方差、协偏度、认知预测失败。

Systematic RSA comparison showing untrained CNNs match backpropagation-trained CNNs at V1 visual cortex. Untrained random-weights CNN (rho=0.076) exceeds backprop (rho=0.034) at V1/V2 (p<0.001). STDP achieves highest V1 alignment among trained rules (rho=0.064). Four learning rules (BP, FA, PC, STDP) compared against human fMRI from THINGS-fMRI dataset (720 stimuli, 3 subjects).

Systematic RSA comparison showing untrained CNNs match backpropagation at V1 alignment with human fMRI. Evaluates BP, FA, PC, and STDP learning rules against THINGS-fMRI dataset using 720 stimuli across 3 subjects. Use when studying brain-model alignment, comparing learning rules, or analyzing visual cortex representations via Representational Similarity Analysis.

Systematic RSA comparison showing untrained CNNs match backpropagation-trained CNNs at V1 visual cortex. Trigger words: untrained CNN, backpropagation, RSA, V1, representational similarity

Systematic RSA comparison showing untrained CNNs match backpropagation-trained networks at V1 visual cortex, revealing architecture's dominant role over learning rules in neural alignment. Activation triggers: untrained cnn, backpropagation, v1, rsa, representational similarity, learning rules, architecture-driven.

Systematic RSA comparison showing untrained CNNs match backpropagation-trained CNNs in V1 visual cortex alignment. Large-scale fMRI analysis reveals that random feature detectors can capture V1 representational structure. Keywords: untrained CNN, V1 cortex, backpropagation, RSA, representational similarity, visual cortex, fMRI.

**arXiv ID:** 2010.15999 **Authors:** Gideon Kowadlo, Abdelrahman Ahmed, David Rawlinson **Published:** 2020-10-30T00:10:23Z **Abstract:** Established experimental procedures for one-shot machine learning do not test the ability to learn or remember specific instances of classes, a key feature of animal intelligence. Distinguishing specific instances is necessary for many real-world tasks, such as remembering which cup belongs to you. Generalisation within classes conflicts with the ability t...

Universal Neural Propagator (UNP) methodology for learning time evolution in many-body quantum systems. Transfers across both Hamiltonians and initial states simultaneously. Activation: neural propagator, quantum dynamics simulation, neural operator learning, quantum state evolution, UNP, universal propagator, quantum foundation model, neural quantum dynamics.

Unifying dynamical systems and graph theory to mechanistically understand computation in neural networks. Combines spectral analysis, community detection, and dynamical systems theory to decompose RNN computation into interpretable sub-circuits. Activation: graph theory neural networks, dynamical systems RNN, mechanistic interpretability, spectral analysis RNN, community detection neural computation.

Systematic methodology for compiling molecular ultrastructure into neural dynamics - bridging microscopic brain structure to computational function. Activation: ultrastructure compiler, molecular neural dynamics, connectome to function, structural biology, neural compilation.

Tsodyks-Markram短时程突触可塑性的混沌动力学。研究确定性TM模型中Shilnikov同宿分岔导致混沌行为的路径，揭示网络动力学不可预测性和对初始条件的敏感性。适用于计算神经科学、突触可塑性建模、混沌动力学分析。触发词：短时程突触可塑性、Tsodyks-Markram模型、Shilnikov分岔、混沌动力学、short-term synaptic plasticity、Tsodyks-Markram model、Shilnikov homoclinic bifurcation、chaotic dynamics。

Triple Configuration Brain Networks (TCBN) framework using RNNs to model synergistic effects of exogenous stimuli, task demands, and spontaneous activity in brain network reconfiguration. Keywords: brain networks, cognitive flexibility, RNN, task-switching, network dynamics.

Treatment-Conditioned Diffusion framework for forecasting neurodegenerative disease progression via high-fidelity brain state prediction. Conditions generative process on DaTscan images and levodopa equivalent daily dose. Activation: neurodegenerative, disease progression, Parkinson, diffusion, longitudinal neuroimaging, DaTscan, treatment-conditioned.

Transcranial photobiomodulation (tPBM) therapy for insomnia using EEG biomarkers. Prefrontal cortex near-infrared light stimulation targeting prefrontal hypoactivity and hyperarousal model. Pilot study with college students using EEG spectral analysis and functional connectivity to elucidate therapeutic mechanisms. Use when: neuromodulation therapy, insomnia treatment, EEG biomarkers, photobiomodulation, prefrontal hypoactivity, hyperarousal model, non-invasive brain stimulation, sleep disord...

Transformer 表征轨迹几何分析方法论 - 将计算神经科学的几何工具应用于 Transformer 可解释性研究，无需探测即可分析表征动力学

Topological Machine Learning for epileptic iEEG seizure detection using persistent homology and persistence diagrams. Features multiple TDA representations and cross-patient generalization. Activation: topological data analysis, TDA, EEG classification, seizure detection, persistent homology.

TMS-EEG生物标志物信效度评估方法论。系统评估TMS-EEG标志物的内部可靠性、外部可靠性和有效性，提供评估框架和最佳实践。触发词：TMS-EEG、生物标志物、可靠性、有效性、信效度、TMS biomarkers、reliability、validity、TMS-EEG analysis。

Thermodynamic framework for analyzing multiplex neural connectomes, linking synaptic and neuropeptidergic signaling layers. Applied to the complete C. elegans connectome to reveal functional specialization and hierarchical organization through energy-based connectivity analysis.

**arXiv ID:** 2407.05650 **Authors:** Pascal J. Sager, Jan M. Deriu, Benjamin F. Grewe, Thilo Stadelmann, Christoph von der Malsburg **Published:** 2024-07-08T06:22:10Z **Abstract:** We introduce the Cooperative Network Architecture (CNA), a model that represents sensory signals using structured, recurrently connected networks of neurons, termed "nets." Nets are dynamically assembled from overlapping net fragments, which are learned based on statistical regularities in sensory input. This arc...

Brain Neural Operator (Tau-BNO) surrogate framework for rapidly approximating Network Transport Model dynamics of pathological tau protein spread in Alzheimer's disease. Combines function operator encoding kinetic parameters with query operator preserving initial state, using spectral kernel for anisotropic transport. Activation triggers: tau propagation, alzheimer modeling, neural operator, brain network transport, biophysical surrogate, disease progression modeling.

Beyond Prediction Accuracy: Target-Space Recovery Profiles for Evaluating Model-Brain Alignment — a framework for identifying which reproducible brain response dimensions are recovered by model predictions, going beyond simple prediction accuracy. (arXiv:2605.20127)