ArXiv Pub Date : 2025-09-23

Parsa Vahidi, Omid G Sani, Maryam M Shanechi

{"title":"BRAID: Input-driven nonlinear dynamical modeling of neural-behavioral data.","authors":"Parsa Vahidi, Omid G Sani, Maryam M Shanechi","doi":"","DOIUrl":"","url":null,"abstract":"Neural populations exhibit complex recurrent structures that drive behavior, while continuously receiving and integrating external inputs from sensory stimuli, upstream regions, and neurostimulation. However, neural populations are often modeled as autonomous dynamical systems, with little consideration given to the influence of external inputs that shape the population activity and behavioral outcomes. Here, we introduce BRAID, a deep learning framework that models nonlinear neural dynamics underlying behavior while explicitly incorporating any measured external inputs. Our method disentangles intrinsic recurrent neural population dynamics from the effects of inputs by including a forecasting objective within input-driven recurrent neural networks. BRAID further prioritizes the learning of intrinsic dynamics that are related to a behavior of interest by using a multi-stage optimization scheme. We validate BRAID with nonlinear simulations, showing that it can accurately learn the intrinsic dynamics shared between neural and behavioral modalities. We then apply BRAID to motor cortical activity recorded during a motor task and demonstrate that our method more accurately fits the neural-behavioral data by incorporating measured sensory stimuli into the model and improves the forecasting of neural-behavioral data compared with various baseline methods, whether input-driven or not.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12486053/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145214597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Probabilistic Geometric Principal Component Analysis with application to neural data. 概率几何主成分分析及其在神经数据中的应用。

ArXiv Pub Date : 2025-09-22

Han-Lin Hsieh, Maryam M Shanechi

{"title":"Probabilistic Geometric Principal Component Analysis with application to neural data.","authors":"Han-Lin Hsieh, Maryam M Shanechi","doi":"","DOIUrl":"","url":null,"abstract":"Dimensionality reduction is critical across various domains of science including neuroscience. Probabilistic Principal Component Analysis (PPCA) is a prominent dimensionality reduction method that provides a probabilistic approach unlike the deterministic approach of PCA and serves as a connection between PCA and Factor Analysis (FA). Despite their power, PPCA and its extensions are mainly based on linear models and can only describe the data in a Euclidean coordinate system around the mean of data. However, in many neuroscience applications, data may be distributed around a nonlinear geometry (i.e., manifold) rather than lying in the Euclidean space around the mean. We develop Probabilistic Geometric Principal Component Analysis (PGPCA) for such datasets as a new dimensionality reduction algorithm that can explicitly incorporate knowledge about a given nonlinear manifold that is first fitted from these data. Further, we show how in addition to the Euclidean coordinate system, a geometric coordinate system can be derived for the manifold to capture the deviations of data from the manifold and noise. We also derive a data-driven EM algorithm for learning the PGPCA model parameters. As such, PGPCA generalizes PPCA to better describe data distributions by incorporating a nonlinear manifold geometry. In simulations and brain data analyses, we show that PGPCA can effectively model the data distribution around various given manifolds and outperforms PPCA for such data. Moreover, PGPCA provides the capability to test whether the new geometric coordinate system better describes the data than the Euclidean one. Finally, PGPCA can perform dimensionality reduction and learn the data distribution both around and on the manifold. These capabilities make PGPCA valuable for enhancing the efficacy of dimensionality reduction for analysis of high-dimensional data that exhibit noise and are distributed around a nonlinear manifold, especially for neural data.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12486060/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145214614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rational Multi-Modal Transformers for TCR-pMHC Prediction. 用于TCR-pMHC预测的理性多模态变压器。

ArXiv Pub Date : 2025-09-22

Jiarui Li, Zixiang Yin, Zhengming Ding, Samuel J Landry, Ramgopal R Mettu

引用次数: 0

Viscoelastic properties of tumor spheroids revealed by a microfluidic compression device and a modified power law model. 用微流控压缩装置和修正幂律模型揭示肿瘤球体的粘弹性特性。

ArXiv Pub Date : 2025-09-22

Mrinal Pandey, Bangguo Zhu, Kaitlyn Roach, Young Joon Suh, Jeffrey E Segall, Chung-Yuen Hui, Mingming Wu

{"title":"Viscoelastic properties of tumor spheroids revealed by a microfluidic compression device and a modified power law model.","authors":"Mrinal Pandey, Bangguo Zhu, Kaitlyn Roach, Young Joon Suh, Jeffrey E Segall, Chung-Yuen Hui, Mingming Wu","doi":"","DOIUrl":"","url":null,"abstract":"Clinically, palpation is one of the important diagnostic methods to assess tumor malignancy. In laboratory research, it is well accepted that the bulk stiffness of the tumor and the surrounding tissue is closely correlated with the malignant state of the tumor. Here, we postulate that, in addition to tumor stiffness, tumor viscoelasticity - the fact that tumor tissue takes time to bounce back after compression, can also be used to evaluate the tumor malignancy state. In this work, we characterized the viscoelastic properties of breast tumor spheroids using a recently developed microfluidic compression device and a theoretical power law model. Breast tumor cells at varying malignant levels; a non-tumorigenic epithelial (MCF10A), moderately malignant tumor (MCF7) and triple negative metastatic tumor (MDA-MB-231) cells were used. Spheroids embedded within a 3D extracellular matrix were periodically compressed, and their strain responses were recorded using microscopic imaging. Our results revealed that the measured strain relaxation curves can be successfully described by a modified power law model, demonstrated that non-tumorigenic tumor spheroids were more elastic, exhibited shorter relaxation time and less plasticity than those of tumorigenic spheroids. Together, these results highlight that viscoelastic properties in addition to bulk stiffness of the tumor spheroids can serve as a complementary mechanical biomarker of tumor malignancy and demonstrate the validity of a modified power law model for the mechanical characterization of a living tissue.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12486062/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145214580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving spliced alignment by modeling splice sites with deep learning. 利用深度学习对剪接位点进行建模，改善剪接比对。

ArXiv Pub Date : 2025-09-20

Siying Yang, Neng Huang, Heng Li

{"title":"Improving spliced alignment by modeling splice sites with deep learning.","authors":"Siying Yang, Neng Huang, Heng Li","doi":"","DOIUrl":"","url":null,"abstract":"Motivation: Spliced alignment refers to the alignment of messenger RNA (mRNA) or protein sequences to eukaryotic genomes. It plays a critical role in gene annotation and the study of gene functions. Accurate spliced alignment demands sophisticated modeling of splice sites, but current aligners use simple models, which may affect their accuracy given dissimilar sequences.Results: We implemented minisplice to learn splice signals with a one-dimensional convolutional neural network (1D-CNN) and trained a model with 7,026 parameters for vertebrate and insect genomes. It captures conserved splice signals across phyla and reveals GC-rich introns specific to mammals and birds. We used this model to estimate the empirical splicing probability for every GT and AG in genomes, and modified minimap2 and miniprot to leverage pre-computed splicing probability during alignment. Evaluation on human long-read RNA-seq data and cross-species protein datasets showed our method greatly improves the junction accuracy especially for noisy long RNA-seq reads and proteins of distant homology.Availability and implementation: https://github.com/lh3/minisplice.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12447723/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145115283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prior-Adapted Progressive Time-Resolved CBCT Reconstruction Using a Dynamic Reconstruction and Motion Estimation Method. 基于动态重建和运动估计方法的先验适应渐进时间分辨CBCT重建。

ArXiv Pub Date : 2025-09-19

Ruizhi Zuo, Hua-Chieh Shao, You Zhang

{"title":"Prior-Adapted Progressive Time-Resolved CBCT Reconstruction Using a Dynamic Reconstruction and Motion Estimation Method.","authors":"Ruizhi Zuo, Hua-Chieh Shao, You Zhang","doi":"","DOIUrl":"","url":null,"abstract":"Cone-beam CT (CBCT) provides on-board volumetric anatomy for image guidance and treatment adaptation in radiotherapy. To compensate for respiration-induced anatomical motion, time-resolved CBCT is highly desired to capture the spatiotemporal anatomical variations but faces challenges in accuracy and efficiency due to substantial optimization needed in image reconstruction and motion modeling. We proposed a fast time-resolved CBCT reconstruction framework, based on a dynamic reconstruction and motion estimation method with new reconstructions initialized and conditioned on prior reconstructions in an adaptive fashion (DREME-adapt). DREME-adapt reconstructs a time-resolved CBCT sequence from a fractional standard CBCT scan while simultaneously generating a machine learning-based motion model that allows single-projection-driven intra-treatment CBCT estimation and motion tracking. Via DREME-adapt, a virtual fraction is generated from a pre-treatment 4D-CT set of each patient for a clean, 'cold-start' reconstruction. For subsequent fractions of the same patient, DREME-adapt uses pre-derived motion models and reference CBCTs as initializations to drive a 'warm-start' reconstruction, based on a lower-cost refining strategy. Three strategies: DREME-cs which drops the 'warm-start' component, DREME-adapt-vfx which uses a fixed initialization (virtual fraction's reconstruction results), and DREME-adapt-pro which initialize reconstructions through a progressive daisy chain scheme (virtual fraction for fraction 1, fraction 1 for fraction 2, and so on), were evaluated on a digital phantom study and a patient study. DREME-adapt allows fast and accurate time-resolved CBCT reconstruction and enhances the clinical adoption potential of the DREME framework.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12458593/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145152405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data coarse graining can improve model performance. 数据粗粒度化可以提高模型性能。

ArXiv Pub Date : 2025-09-18

Alex Nguyen, David J Schwab, Vudtiwat Ngampruetikorn

{"title":"Data coarse graining can improve model performance.","authors":"Alex Nguyen, David J Schwab, Vudtiwat Ngampruetikorn","doi":"","DOIUrl":"","url":null,"abstract":"Lossy data transformations by definition lose information. Yet, in modern machine learning, methods like data pruning and lossy data augmentation can help improve generalization performance. We study this paradox using a solvable model of high-dimensional, ridge-regularized linear regression under data coarse graining. Inspired by the renormalization group in statistical physics, we analyze coarse-graining schemes that systematically discard features based on their relevance to the learning task. Our results reveal a nonmonotonic dependence of the prediction risk on the degree of coarse graining. A high-pass scheme-which filters out less relevant, lower-signal features-can help models generalize better. By contrast, a low-pass scheme that integrates out more relevant, higher-signal features is purely detrimental. Crucially, using optimal regularization, we demonstrate that this nonmonotonicity is a distinct effect of data coarse graining and not an artifact of double descent. Our framework offers a clear, analytical explanation for why careful data augmentation works: it strips away less relevant degrees of freedom and isolates more predictive signals. Our results highlight a complex, nonmonotonic risk landscape shaped by the structure of the data, and illustrate how ideas from statistical physics provide a principled lens for understanding modern machine learning phenomena.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12458590/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145152387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A High-Order Cumulant Extension of Quasi-Linkage Equilibrium. 准连杆平衡的高阶累积量扩展。

ArXiv Pub Date : 2025-09-18

Kai S Shimagaki, Jorge Fernandez-de-Cossio-Diaz, Mauro Pastore, Rémi Monasson, Simona Cocco, John P Barton

{"title":"A High-Order Cumulant Extension of Quasi-Linkage Equilibrium.","authors":"Kai S Shimagaki, Jorge Fernandez-de-Cossio-Diaz, Mauro Pastore, Rémi Monasson, Simona Cocco, John P Barton","doi":"","DOIUrl":"","url":null,"abstract":"A central question in evolutionary biology is how to quantitatively understand the dynamics of genetically diverse populations. Modeling the genotype distribution is challenging, as it ultimately requires tracking all correlations (or cumulants) among alleles at different loci. The quasi-linkage equilibrium (QLE) approximation simplifies this by assuming that correlations between alleles at different loci are weak - i.e., low linkage disequilibrium - allowing their dynamics to be modeled perturbatively. However, QLE breaks down under strong selection, significant epistatic interactions, or weak recombination. We extend the multilocus QLE framework to allow cumulants up to order <math><mi>K</mi></math> to evolve dynamically, while higher-order cumulants <math> <mrow> <mfenced><mrow><mo>></mo> <mi>K</mi></mrow> </mfenced> </mrow> </math> are assumed to equilibrate rapidly. This extended QLE (exQLE) framework yields a general equation of motion for cumulants up to order <math><mi>K</mi></math> , which parallels the standard QLE dynamics (recovered when <math><mrow><mi>K</mi> <mo>=</mo> <mn>1</mn></mrow> </math> ). In this formulation, cumulant dynamics are driven by the gradient of average fitness, mediated by a geometrically interpretable matrix that stems from competition among genotypes. Our analysis shows that the exQLE with <math><mrow><mi>K</mi> <mo>=</mo> <mn>2</mn></mrow> </math> accurately captures cumulant dynamics even when the fitness function includes higher-order (e.g., third- or fourth-order) epistatic interactions, capabilities that standard QLE lacks. We also applied the exQLE framework to infer fitness parameters from temporal sequence data. Overall, exQLE provides a systematic and interpretable approximation scheme, leveraging analytical cumulant dynamics and reducing complexity by progressively truncating higher-order cumulants.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12458595/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145152342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mouse vs. AI: A Neuroethological Benchmark for Visual Robustness and Neural Alignment. 小鼠与人工智能：视觉稳健性和神经对齐的神经行为学基准。

ArXiv Pub Date : 2025-09-17

Marius Schneider, Joe Canzano, Jing Peng, Yuchen Hou, Spencer LaVere Smith, Michael Beyeler

{"title":"Mouse vs. AI: A Neuroethological Benchmark for Visual Robustness and Neural Alignment.","authors":"Marius Schneider, Joe Canzano, Jing Peng, Yuchen Hou, Spencer LaVere Smith, Michael Beyeler","doi":"","DOIUrl":"","url":null,"abstract":"Visual robustness under real-world conditions remains a critical bottleneck for modern reinforcement learning agents. In contrast, biological systems such as mice show remarkable resilience to environmental changes, maintaining stable performance even under degraded visual input with minimal exposure. Inspired by this gap, we propose the Mouse vs. AI: Robust Foraging Competition, a novel bioinspired visual robustness benchmark to test generalization in reinforcement learning (RL) agents trained to navigate a virtual environment toward a visually cued target. Participants train agents to perform a visually guided foraging task in a naturalistic 3D Unity environment and are evaluated on their ability to generalize to unseen, ecologically realistic visual perturbations. What sets this challenge apart is its biological grounding: real mice performed the same task, and participants receive both behavioral performance data and large-scale neural recordings (19,000+ neurons across visual cortex) for benchmarking. The competition features two tracks: (1) Visual Robustness, assessing generalization across held-out visual perturbations; and (2) Neural Alignment, evaluating how well agents' internal representations predict mouse visual cortical activity via a linear readout. We provide the full Unity environment, a fog-perturbed training condition for validation, baseline proximal policy optimization (PPO) agents, and a rich multimodal dataset. By bridging reinforcement learning, computer vision, and neuroscience through a shared, behaviorally grounded task, this challenge advances the development of robust, generalizable, and biologically inspired AI.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12458599/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145152359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sensitivity of literature $T_1$ mapping methods to the underlying magnetization transfer parameters. 文献$T_1$映射方法对底层磁化传递参数的敏感性。

ArXiv Pub Date : 2025-09-17

Jakob Assländer

{"title":"Sensitivity of literature $T_1$ mapping methods to the underlying magnetization transfer parameters.","authors":"Jakob Assländer","doi":"","DOIUrl":"","url":null,"abstract":"Purpose: Magnetization transfer (MT) has been identified as the principal source of $T_1$ variability in the MRI literature. This study assesses the sensitivity of established $T_1$ mapping techniques to variations in the underlying MT parameters.Methods: For each $T_1$-mapping method, the observed $T_1$ was simulated as a function of the underlying MT parameters $p_i^text{MT}$, corresponding to different brain regions of interest (ROIs) at 3T. As measures of sensitivity, the derivatives $partial T_1^text{observed} / partial p_i^text{MT}$ were computed and analyzed with a linear mixed-effects model as a function of $p_i^text{MT}$, ROI, pulse sequence type (e.g., inversion recovery, variable flip angle), and the individual sequences.Results: The analyzed $T_1$-mapping sequences have a considerable sensitivity to changes in the semi-solid spin pool size $m_0^text{s}$, $T_1^text{f}$ of the free, $T_1^text{s}$ of the semi-solid spin pool, and the (inverse) exchange rate $T_text{x}$. All derivatives vary considerably with the underlying MT parameters and between pulse sequences. The derivatives can, in general, not be determined by the sequence type, but rather depend on implementation details of the sequence. One notable exception is that variable-flip-angle methods are, in general, more sensitive to the exchange rate than inversion-recovery methods.Conclusion: Variations in $T_1$ measurements can be caused by several underlying MT parameters, and the sensitivity to each parameter depends on both the underlying MT parameters and the sequence.","PeriodicalId":93888,"journal":{"name":"ArXiv","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12458585/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145152174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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