Neurons and Cognition
Effects of cortisol time-scales in a model of emotion regulation
Michael Buccino, Joshua Ginart, Eva Kaslik et al. (+1 more)
Stress reshapes cortico-limbic dynamics across multiple timescales, with important consequences for healthy and pathological arousal responses. We develop and analyze a Wilson-Cowan model of prefrontal-amygdala circuitry, incorporating excitatory and inhibitory populations involved in emotional regulation. The effects of stress cortisol are represented as history-dependent modulations of two stress-sensitive interactions: recurrent prefrontal excitation and inhibition of basolateral amygdala principal
cells. Using numerical simulations and bifurcation analysis, we examine how this self-interacting system organizes transitions between stable activity and infra-slow oscillations, and how this structure changes with cortisol-related temporal
memory. To distinguish the effects of
memory temporal distribution from those of its timescale, we compare weak and strong Gamma kernels with a discrete-delay limit, representing progressively different ways in which past activity can influence current coupling. Introducing temporal
memory reshapes the parameter region supporting oscillations and alters their persistence. Weak and strong Gamma kernels produce remarkably similar global dynamics, whereas the discrete-delay limit generates a substantially richer bifurcation structure, including quasiperiodic activity. Together, these results suggest that the dynamical effects of cortisol depend not only on the characteristic timescale of its action, but also on the shape of the temporal
memory through which past neural activity influences the present. Different forms of temporal integration may provide a mechanism by which neural systems implement distinct functional dynamics, while changes in this integration could promote atypical or pathological rhythmic regimes.
Read the full abstract ▾
All authors Michael Buccino, Joshua Ginart, Eva Kaslik, Anca Radulescu
Quantitative Methods
An Open-Source Web-Based Viewer for Disseminating Image-Derived Models
Christian Herz, Kyle Sunderland, Matthew Daemer et al. (+11 more)
Patient-specific 3-dimensional (3D) models are increasingly central to planning surgery and catheter-based intervention in children with congenital heart disease (CHD), whose anatomy is often atypical and variable. These models inform care only when the surgeons and interventional cardiologists who make treatment decisions can rotate, crop, and measure them directly. Desktop modeling applications require local installation, capable hardware, and training. Commercial web-based viewers are portable but closed, and, in our experience, cannot display volume rendering, time-resolved (e.g., 4-dimensional) data, or the custom workflows centers build for CHD. Because CHD populations are small and heterogeneous, such workflows are unlikely to be prioritized commercially, and end users have no means to add them. To address these limitations, we developed the SlicerHeart Viewer, an open-source module that presents a simplified interface to a complete 3D Slicer/SlicerHeart session running on a server. Clinicians access models created by clinical modeling services from any device through a web browser. Because 3D Slicer runs underneath, volume rendering, time-resolved data, and any SlicerHeart workflow, including those developed for CHD, are available without modification. The module is independent of the hosting environment; we chose internal servers behind institutional single sign-on, and a cloud-hosted option is in development. Over one year at a single pediatric center, nearly all of 82 modeled cases were shared through the Viewer with clinicians from five specialties, with cases loading in 5 to 10 seconds. The approach is not specific to CHD and is deployable at other centers.
Read the full abstract ▾
All authors Christian Herz, Kyle Sunderland, Matthew Daemer, Julia Iacovella, Sehdev Pressley, Ashley Koluda, Csaba Pinter, Kevin Zuber, John Daniels, Allen Ladd, Mali Hetmaniuk, Kevin K. Whitehead, Andras Lasso, Matthew A. Jolley
Neurons and Cognition
Simulating Alzheimer's amyloid-like pathology on a fly central nervous system connectome
Taeho Jo
In Alzheimer's disease, the amount of pathology in the
brain is an imperfect predictor of how much function is lost. Pathology leads to that loss through neural
circuits, but these cannot be observed
neuron by
neuron in patients. This study therefore simulated the central nervous system of the adult male Drosophila melanogaster: 166,700 integrate-and-fire
neurons wired by the MaleCNS connectome. The simulator reproduced a published fly
brain model (rate correlation 0.9997). Three kinds of damage reported in amyloid-$\beta$ flies, a lowered spike threshold, synapse loss and
neuron loss, were imposed with assumed magnitudes, and
circuit responses were measured in 1,424 simulated flies. The number of
neurons lost was a poor guide to the deficit, which depended partly on how many synaptic contacts went with them and whether they included input
neurons or one command
neuron. With the same 12% of
neurons removed, odour-evoked whole-
brain activity was 93%, 47% or 31% of the healthy value when damage fell on poorly connected, random or highly connected
neurons; these flies lost 20%, 35% and 48% of contacts. When the contacts lost were equalised, the three groups differed less; whether the remaining difference reflects where the damage fell could not be established. In 48 flies, restoring only the contacts that surviving
neurons had lost recovered 62% of the whole-
brain deficit, and restoring the removed
neurons 39%. In flies that had lost
neurons delivering the odour, these accounted for about half the deficit due to
neuron loss. One
neuron could decide an outcome: all 66 flies that lost the giant fibre on the stimulated side, which commands escape, had a giant fibre rate below 100 Hz, against 1 of 254 that kept it. Whole-
brain activity revealed damage at milder severity than the learning index, because it changed much relative to its variation between trials.
Read the full abstract ▾
Populations and Evolution · Cross-listed from cs.LG
Deep Generative Models for Phylogenetic Inference with Complex Evolutionary Processes
Ethan Baron, Alan N. Amin and Andrew Gordon Wilson
Inference of phylogenetic trees from sequences plays a central role in understanding evolutionary relationships, with applications ranging from tracking pathogen spread to reconstructing the history of life. Conventionally, practitioners obtain posteriors over trees using likelihood-based methods like MCMC. However, likelihoods are only available for simple evolutionary models with restrictive assumptions. For realistic evolutionary models, inference using conventional methods is prohibitively expensive, inaccurate, or even impossible. In practice, practitioners settle for using as flexible a conventional method as possible, even though they expect it is misspecified. In principle, we could use simulation-based inference: simulate tree and sequence data from an evolutionary model and train a neural network to predict the posterior over trees given sequences. However it is a mathematical challenge to represent and train complex posterior distributions over trees. Here we present a flexible family of conditional generative models over trees. Our models iteratively build trees using three natural paradigms – top-down, middle-out, or bottom-up – and are trained using a discrete
diffusion framework. For real and synthetic
datasets of up to $65$ taxa and $>10^5$
tokens, our models recover MCMC posteriors much more accurately than the previous state-of-the-art simulation-based method Phyloformer 2. Finally, we compare to IQ-Tree, a popular flexible-but-misspecified conventional method. Our models match its tree estimates on simple data, but on more realistic data, our method is reliably more accurate.
Read the full abstract ▾
Molecular Networks
Deep learning-based virtual perturbation reveals increasing heterogeneity of gene regulatory responses with aging
Yongjian He, Julia Hanna Haddad Guez, Xu-Wen Wang et al. (+1 more)
A central hypothesis in aging biology proposes that the gradual accumulation of
stochastic molecular damage drives transcriptional deregulation (TD), ultimately leading to functional decline. However, existing single-
cell analyses largely rely on static expression measurements and provide limited resolution of how regulatory responses change at the level of individual
cells. Here, we leverage DKO, a deep learning framework that predicts
cell-specific virtual gene perturbation effects from unperturbed transcriptomes, capturing
cell-to-
cell differences in regulatory context without requiring a reconstructed gene regulatory network or matched perturbation data. Applying DKO to single-
cell transcriptomic
datasets from humans, mice, Drosophila, and C. elegans revealed that the transcriptional consequences of perturbing the same gene became increasingly heterogeneous across
cells with age, indicating that gene regulatory responses become less consistent and more dependent on cellular context. Furthermore, we find that
cells with higher somatic mutation burdens have greater knockout-response variability, linking accumulated molecular damage to regulatory inconsistency. Within the same cellular populations, higher impact variability was also associated with higher senescence scores, linking transcriptional dysregulation to senescence-associated states. Our findings identify a progressive loss of regulatory robustness at both the gene and
cell levels as a conserved feature of aging, linking
stochastic molecular damage to increasing heterogeneity in cellular regulatory responses.
Read the full abstract ▾
All authors Yongjian He, Julia Hanna Haddad Guez, Xu-Wen Wang, Orr Levy
Quantitative Methods · Cross-listed from cs.LG
PMO-Dock: Benchmarking Docking, Specificity, and Generalization in Molecular Optimization
Gor Simonyan, Tatevik Abrahamyan, Narek Abelyan et al. (+2 more)
The Practical Molecular Optimization (PMO)
benchmark standardized evaluation in molecular optimization, but it is built on simple property-based oracles that do not capture structure-based drug design. As the field has moved to docking-based objectives, evaluation practice has become unstandardized, and different methods report results on different docking tasks, often without a shared protocol for fair comparison or sample-efficiency constraints. To address this, we introduce Practical Molecular Optimization for Docking (PMO-Dock), a
benchmark and protocol for docking-based optimization consisting of 25 tasks covering hit generation, lead optimization, and a new specificity task requiring strong on-target binding while penalizing off-target interactions. The protocol separates model development from final evaluation: validation tasks are used for hyperparameter tuning, and test tasks are held out for final evaluation and are not used during development. Evaluation on the test tasks is done under a strict oracle budget to reflect realistic drug-discovery settings. We
benchmark four diverse high-performing methods spanning different optimization paradigms, Saturn (reinforcement learning), GenMol (discrete
diffusion), Genetic-guided GFlowNet, and ChemLlama (LLM-based evolutionary search). Our results show no universally best method across tasks, additional analyses of hyperparameter transfer (cross-task and cross-category) and sensitivity show that transfer is often limited and varies by method, motivating method-dependent selection strategies. This
benchmark provides a concrete standard for evaluating generalization to unseen tasks and sample efficiency under a limited test-time oracle budget.
Read the full abstract ▾
All authors Gor Simonyan, Tatevik Abrahamyan, Narek Abelyan, Tigran Fahradyan, Hrant Khachatrian
Tissues and Organs
Modeling the Microenvironmental Dynamics of Adenoid Cystic Carcinoma in Lacrimal Gland with Treatment Intervention
Ayasa Akter, Miss Sajnin Binta Salam Efty and Uzzwal Kumar Mallick
Adenoid cystic carcinoma (ACC) of the lacrimal gland is a rare and extremely life threatening malignant tumor with a high risk of recurrence and poor long-term outcomes. On the other hand, due to its complex molecular dynamics and low incidence rate, treatment strategies are very limited. In this study, a mathematical model is developed based on the biological phenomena of the tumor microenvironment using nonlinear ordinary differential equations. It is analyzed both analytically and numerically. According to the findings, the growth of tumor
cells is associated with the depletion of oxygen and nutrients, while severe hypoxia promotes the development of abnormal blood vessels and makes tumor control more difficult. Surgery and postoperative radiotherapy have been considered as treatment strategies, and we have observed that surgery alone may not be sufficient to prevent the regrowth of residual tumor
cells. In addition, postoperative radiotherapy provides better control of the residual tumor burden, particularly when the remaining tumor
cells have higher radiosensitivity. Furthermore, different radiotherapy interruption scenarios have been investigated, and the results indicate that longer gaps between radiotherapy doses can adversely affect the overall treatment response, leading to increased overall toxicity and a higher number of residual tumor
cells at the end of the observation period. Consequently, our results suggest that postoperative radiotherapy after surgery, combined with adequate radiosensitivity of the residual tumor
cells and adherence to the prescribed treatment schedule without prolonged interruptions, may reduce the risk of tumor recurrence and potentially improve long term survival.
Read the full abstract ▾
Quantitative Methods · Cross-listed from cs.AI
MFG: Multi-turn Feedback Guided Molecular Optimization
Trieu Nguyen, Frazier N. Baker, Ruoxi Gao et al. (+2 more)
Multi-objective molecular optimization is a critical process in drug discovery that aims to modify a given molecule to simultaneously improve multiple properties while maintaining a similar overall structure. This process often requires iterative modifications guided by computational or experimental feedback. Recent large
language model (LLM)-based methods have begun to explore this iterative approach; however, these methods are not explicitly trained to leverage such feedback, while existing approaches primarily focus on single-turn molecular optimization. To address this gap, we introduce Multi-turn Feedback Guided (MFG) molecular optimization, a reinforcement learning framework that trains an LLM to progressively optimize molecules using feedback from previous turns. MFG introduces a feedback vector capturing both property improvements and source molecule similarity, alongside a feedback-aligned reward function that promotes progressive improvement across turns. We apply MFG over $\text{GeLLM}^{3}\text{O}$, a state-of-the-art LLM model for multi-objective molecular optimization, evaluate $\text{GeLLM}^{3}\text{O-MFG}$ on ten multi-objective molecular optimization tasks. The results demonstrate that $\text{GeLLM}^{3}\text{O-MFG}$ achieves the highest average similarity-constrained success rate ($81.50%$), outperforming the base model $\text{GeLLM}^{3}\text{O}$ ($70.81%$).
Read the full abstract ▾
All authors Trieu Nguyen, Frazier N. Baker, Ruoxi Gao, Daniel Adu-Ampratwum, Xia Ning
Quantitative Methods
Brain Age Gap Progression in Individuals at Risk for Neurodegeneration
Jason Scheffel and Saurabh Sihag
Neuroimaging is a widely used modality for characterizing neurodegeneration in various health conditions.
Brain age gap has emerged as a promising biomarker for monitoring
brain health from neuroimaging data using machine learning
algorithms. Recent studies have leveraged coVariance neural networks (VNN)-driven pipelines based on the advancements in graph signal processing for relatively transparent construction of
brain age gap from neuroimaging data. In this paper, we focus on a longitudinal study of VNN-based
brain age gap in conjunction with Alzheimer's Progression Score (APS) in individuals that are at a higher risk of neurodegeneration due to family history of Alzheimer's disease. APS is a
multimodal marker of disease progression. The longitudinal analysis of APS and VNN-derived
brain age gap metrics revealed that a higher risk of disease progression at baseline predicted a larger rate of increase in
brain age gap in follow-up visits. Thus, our findings revealed potential stratification in the
brain age gap modulated by clinical/biological risks of AD in the at-risk population.
Read the full abstract ▾
Neurons and Cognition · Cross-listed from cs.AI
Two Routes to Interoceptive Prioritisation: Dissociating Likelihood Precision and Preference Reweighting in Active Inference
St John Grimbly, Nicolas Kuske, Evert A. Boonstra et al. (+6 more)
An embodied
agent must prioritise among competing bodily needs. In active inference, it can respond to an urgent need by increasing the precision of information about that need or by increasing its preference for outcomes that relieve it. We compare these two interventions, separately and together, in a foraging gridworld with three lethal needs, and ask how each changes survival, perception and the body model the
agent learns. Precision sharpens interoceptive posteriors and improves survival. Preference reweighting keeps bodily states further from their lethal boundaries and changes the states from which the
agent learns, so its learned model covers fewer need levels, with lower quality across all levels but slightly higher quality on the levels it visits. An experiment on 72 further layouts does not establish the predicted preference benefit on difficult layouts, but both interventions improve average survival there and their interaction is positive. Survival alone therefore gives an incomplete account of how a model prioritises needs and learns about its body.
Read the full abstract ▾
All authors St John Grimbly, Nicolas Kuske, Evert A. Boonstra, Charel van Hoof, Rowan Hodson, Benjamin Rosman, Ryan Smith, Mark Solms, Jonathan P. Shock
Quantitative Methods
STAMP as a topology-aware molecular language for fragment-level learning and editing
Yuxuan Ren, Jiatong Li, Ziqiao Meng et al. (+1 more)
Molecular fragments provide chemically meaningful units, but existing molecular
languages rarely make their locations and connections explicit for editing. Here we introduce STAMP (Sequential Tree Attachment-based Motif-Preserving representation), a
topology-aware molecular
language that connects fragment-level representation learning with precise structural editing. STAMP separately encodes motif identity, attachment sites and tree
topology, making fragments reusable across contexts and subtrees directly accessible. Learned representations capture motif similarity and recurring transformations, and lower activity-cliff prediction error on 23 of 30 targets versus a matched SMILES encoder. Compatible subtree operations support scaffold-preserving optimization, ranking first among six methods on 15 of 23
benchmark objectives. A deterministic compiler enables frozen
language models to act on indexed fragments, improving molecular editing, optimization, transformation transfer and change identification by 9 to 56 percentage points over plain SMILES. Together, these results establish an executable molecular
language in which learned chemical units remain directly operable during design.
Read the full abstract ▾
All authors Yuxuan Ren, Jiatong Li, Ziqiao Meng, Yatao Bian
Molecular Networks
The influence of decay in dynamic models of gene regulatory networks
Oleksandr Pokutnyi and Felix Sadyrbaev
We investigate the influence of decay rates on the dynamics of three-dimensional gene regulatory networks modeled by nonlinear differential equations with logistic sigmoidal regulatory functions. Particular
attention is paid to the existence, location, and stability of
equilibrium points in systems involving numerous interacting parameters. An explicit analytical representation of a central
equilibrium is obtained under suitable relations between the regulatory weights, thresholds, and decay coefficients, allowing its location to be determined without solving the corresponding nonlinear algebraic system numerically. General formulas for the characteristic
polynomial of the linearized system are derived, and spectral conditions for instability are established for several classes of regulatory interactions. We demonstrate how variations in decay rates can change the number and stability of
equilibria, transform oscillatory dynamics, and influence the emergence or suppression of complicated attractors. The possibility of chaotic behavior is investigated through
eigenvalue configurations, Lyapunov exponents, and the Kaplan-Yorke dimension. Numerical examples illustrate the sensitivity of the dynamics to decay parameters, including the suppression of previously observed chaotic behavior. The results provide an analytical framework for investigating
equilibrium configurations, stability transitions, and complex dynamics in parametrized gene regulatory networks.
Read the full abstract ▾
Quantitative Methods
An efficient and flexible generalization of transit compartment models for delayed biological processes
Giulia Belluccini, Christiaan H. van Dorp, Linjie Huang et al. (+3 more)
Transit compartment models are widely used in pharmacokinetics/pharmacodynamics to represent delayed biological processes such as absorption, maturation, cellular proliferation, and treatment-induced toxicity. This transit compartment formulation imposes an artificial relationship between the mean and variance of the delay distribution, as the number of transit compartment must be integer-valued number of transit compartments. Consequently, estimating the number of compartments from experimental data leads to mixed-integer optimization problems that artificially constrain model dynamics and bias parameter inference. However, these transit compartment models are mathematically equivalent to distributed delay differential equations with Erlang delay kernels. We leverage a hypoexponential generalization of these distributed delay differential equations that preserves the familiar ordinary differential equation structure while allowing the mean and variance of the delay distribution to be estimated directly as real-valued parameters. This approach does not increase the number of inferred parameters and can be immediately implemented as a straightforward modification of existing transit compartment models. We demonstrate the approach using models of viral eclipse phases, lymphocyte proliferation, and chemotherapy-induced neutropenia. Across these examples, the hypoexponential formulation provides comparable or improved fits relative to Erlang transit models and substantially reduces computational cost by avoiding grid search over compartment numbers. This framework provides a practical approach to estimate biologically meaningful delay distributions in quantitative systems pharmacology (QSP) models.
Read the full abstract ▾
All authors Giulia Belluccini, Christiaan H. van Dorp, Linjie Huang, Jodie A. Cochrane, Sara Hamis, Tyler Cassidy
Neurons and Cognition
Sense-Making phase transition framework for Affect
Miguel Bosch, Laura Fanfarillo, Gustavo Diez et al. (+3 more)
Contemporary accounts of affect emphasize different perspectives, ranging from the regulation of bodily states in allostatic approaches to the relational and sense-making character stressed by enactive theories. Here, we show that affect can be made explicit within the sense-making phase transition framework previously introduced for the perception of an inner sensation. Within this same formal structure, each stable minimum of the sense-making potential defines an affective state: its position determines valence, while its depth determines arousal. Affect therefore is not introduced as an independent ingredient, but emerges as the associated state in the valence-arousal space, providing a link between allostatic and enactive approaches to affect. This formulation distinguishes valence intensity from arousal, allows for dominant and secondary affective states, and naturally accommodates mixed-valence configurations beyond a strictly bipolar description. Revisiting representative landscapes of the model, we show how neutral, uncertain, polarized, and mixed-valence regimes arise as a function of contextual sensory information, learned historical evaluation, and externally supplied information. We further show that the resulting valence-arousal relation provides a theoretical basis for the upper-open V-shaped organization discussed in the affect literature, while also opening a natural route toward a dynamical description of affect and emotions.
Read the full abstract ▾
All authors Miguel Bosch, Laura Fanfarillo, Gustavo Diez, Victor Gómez Mayordomo, J. Ricardo Arias-Gonzalez, Belén Valenzuela
Populations and Evolution · Cross-listed from quant-ph
Lossless Reductions and Hybrid Sampling for Phylogeny Using the Maximum Compatibility Criterion
Orson Ye, James Bonfield, Joshua Cudby et al. (+2 more)
Maximum compatibility provides a criterion for phylogenetic reconstruction in which the objective is to retain a maximum set of mutually compatible characters. For binary characters, pairwise compatibility can be represented by a weighted graph whose vertices correspond to characters and whose edges connect compatible pairs. A leading
algorithm by Cherry [BMC Bioinformatics, 18(1):127, 2017] uses this representation to identify maximum compatible character sets as maximum weight cliques. We formulate the problem as maximum weight independent set enumeration on the complement graph and use deterministic postprocessing to map sampled bit strings to maximal feasible solutions. To explore the prospects of
quantum computational advantage, we further construct bespoke correction-in-the-loop
Quantum Approximate Optimisation
Algorithm instances and find that traditional Lagrange multiplier choices may not be optimal for the enumeration problem on noisy devices. We also introduce lossless graph reductions with reconstruction rules that preserve the complete set of optimal solutions. Our results indicate that graph reduction and sampling can be effectively combined to address the enumeration bottleneck in maximum compatibility phylogeny, while retaining exact reconstruction when the residual optimum set is completely resolved. We enhance Cherry's approach to maximum compatibility by introducing graph reduction techniques and
quantum sampling. Our classical
benchmarks show a clear difference between finding one optimum and recovering the optimum set in practice. We demonstrate the utility of our methods on graphs constructed from SARS-CoV-2 sequences.
Read the full abstract ▾
All authors Orson Ye, James Bonfield, Joshua Cudby, Richard Durbin, Sergii Strelchuk
Neurons and Cognition
Rewiring of adult neural networks - learning from structural plasticity across multiple scales
Michael Fauth, Han Lu, Jannik Luboeinski et al. (+36 more)
Synaptic connections are continuously formed and eliminated even in adult neuronal networks. This process - called structural plasticity - allows networks to change their
topology to learn and adapt to their environment or compensate for lesions. Yet, in contrast to synaptic plasticity, structural plasticity is less well studied and understood. We here present the current state of knowledge as well as open problems in the field along three guiding questions: (1) What regulates structural plasticity in biological neural networks? (2) What are the functional consequences and advantages of structural plasticity in adult neural networks? (3) How can our understanding of structural plasticity be translated into clinical and technological applications? We argue that understanding structural plasticity is a problem involving multiple temporal and spatial scales. Solving this problem thus requires collaborative efforts of experimentalists, theoreticians as well as computational modelers, and has the potential for comprehensive neuroscientific insights that can guide the development of medical applications, artificial neural networks as well as neuromorphic computing.
Read the full abstract ▾
All authors Michael Fauth, Han Lu, Jannik Luboeinski, Johanna Senk, Simone Albani, Valentina Armuzza, Paolo Carloni, Richmond L. Crisostomo, Hermann Cuntz, Ulrich Egert, Eugenio F. Fornasiero, Tobias Gemmeke, Claus C. Hilgetag, Sabine M. Hölter, Patrick Inoue, Björn Kampa, James C. Knight, Susanne Kunkel, Thomas Nowotny, Samora Okujeni, Nicola Palomero-Gallagher, Mariia Popova, Petra Ritter, Giulia Rossetti, Michael Schirner, Marc Spehr, Leon Stefanovski, Gaia Tavosanis, Agata Witkowska, Felix Wolf, Willem Wybo, Sandra Diaz-Pier, Maximilian Lenz, Abigail Morrison, Florian Röhrbein, Stefan Rotter, Johannes Schemmel, Christian Tetzlaff, Andreas Vlachos
Genomics · Cross-listed from stat.ME
Spatial Kernel Restricted Canonical Correlation Analysis: Population Targets, Identifiability, and Stability
Zhen Miao, Yilong Qu and Nancy R. Zhang
Relating observations of different categories across space requires methods that accommodate distinct sampling locations and spatial dependence. Spatial kernel restricted canonical correlation analysis (skrCCA) uses spatial weights to identify associated feature combinations across data categories. Here, we develop a statistical theory of its covariance form after feature reduction. Conditional on the observed locations and under a mean-zero model, the pairwise association
matrices target kernel-weighted averages of cross-covariances. For prespecified directions, we derive the exact null variance under independence between blocks. We establish recovery and stability bounds in two- and multiblock settings under explicit population models, and show that exact cyclic updates have convergent objective values. The variance characterization shows how spatial weighting and within-block dependence jointly determine the effective information available for estimating association. The recovery bounds separate statistical and optimization error, relating stability to population separation and the strength of connections between blocks. These results clarify what fitted spatial association axes estimate and provide conditions under which they can be recovered reliably.
Read the full abstract ▾
Quantitative Methods · Cross-listed from cs.AI
ECG-RSI: Recursive Self-Improvement through Targeted Electrocardiogram Waveform Evolution
Xiaoliang Wang, Zhuo Chen, Chuanyang Zheng et al. (+8 more)
Electrocardiogram (ECG) models need supervision that targets evolving weaknesses and remains faithful to waveform evidence. We introduce ECG-RSI, a recursive self-improvement framework combining verified waveform synthesis with feedback-guided intervention search. Signal remeasurement verifies waveform edits; construction errors guide generator repair, while learner errors motivate interventions compared on a separate selection set. Across seven
benchmarks, three rounds raise Qwen3.5-35B-A3B's mean question-answering accuracy from 34.57% to 56.04% and mean classification F1 from 24.27 to 34.03. Across the retained trajectory, the preferred supervision shifts from direct reinforcement to confusion-focused contrasts. Continued feedback correction yields diminishing external gains. These results motivate separating error diagnosis from intervention selection and checking whether gains on the feedback set transfer to independent
benchmarks.
Read the full abstract ▾
All authors Xiaoliang Wang, Zhuo Chen, Chuanyang Zheng, Jiangwei Lao, Quanbin Wang, Honglin Lin, Jiajia Liu, Zhihong Lu, Hao Jin, Jian Wang, Lijun Wu
Genomics · Cross-listed from cs.AI
Understanding Gene Expression-to-Image Learning: Spatial Structure and Information Fidelity
Xiaoqing Lian, Pei Liu, Jiahui Tan et al. (+3 more)
Single-
cell RNA sequencing (scRNA-seq) enables
cell-level analysis of biological heterogeneity, but learning effective representations from high-dimensional, unordered gene expression profiles remains challenging. Gene Expression-to-Image (GE2I) offers an effective learning strategy by arranging gene values on pixel grids for convolutional neural networks, but its underlying mechanisms remain unclear. We study GE2I on full-transcriptome data while keeping the network and training procedure fixed. Our results reveal a trade-off between spatial smoothness and gene-level fidelity. Structured mappings produce smooth, low-frequency patterns that support faster early optimization, whereas assigning multiple genes to the same pixel obscures expression differences and can reduce predictive performance. This trade-off explains why random mappings can compete with structured layouts when many genes are represented. One-to-one gene-to-pixel assignment improves prediction across the tested visual backbones even when image smoothness decreases. We further find that the benefits of pretraining and augmentation depend on the backbone and mapping. These findings provide practical guidance for pixel allocation and the transfer of
vision priors to gene expression data.
Read the full abstract ▾
All authors Xiaoqing Lian, Pei Liu, Jiahui Tan, Quan Zou, Chen Lin, Xiangxiang Zeng
Quantitative Methods · Cross-listed from cs.AI
MedEvo: Medical Reasoning Evolution through Knowledge Navigation
Xiaoliang Wang, Zhuo Chen, Chuanyang Zheng et al. (+10 more)
Medical
multimodal reasoning models are typically trained on teacher-generated rationales, discarding the valid
reasoning in students' own failed trajectories, while general-domain correction methods offer little guidance for errors in image reading, anatomical context, or differential diagnosis. We propose MedEvo, which uses medical knowledge, namely a tag taxonomy and error-type diagnosis, to navigate both trajectory repair and data selection. For each failed rollout, a gold-blind auditor locates the first consequential error and provides a minimal, answer-free hint; the same student retains the valid prefix and regenerates the erroneous suffix. After each round, MedEvo identifies capability gaps across medical tags on a held-out feedback set and prioritizes related questions for new rollouts and repairs. With supervised fine-tuning (SFT) alone, MedEvo raises Qwen3.5-9B mean accuracy across seven
benchmarks from 65.57% to 69.12% (47.40% to 54.50% on the
multimodal track of MedXpertQA (MedXpertQA-MM)), surpassing teacher-
distillation SFT (66.93%). It also raises Qwen3.5-35B and Qwen3.5-397B mean accuracy from 67.91% to 73.04% and from 71.83% to 74.95%, respectively. Our analyses show that failed trajectories contain
reasoning worth preserving, that repair success does not imply learning value, and that independent feedback is key to sustained improvement.
Read the full abstract ▾
All authors Xiaoliang Wang, Zhuo Chen, Chuanyang Zheng, Quanbin Wang, Jiangwei Lao, Yingao Liu, Jiajia Liu, Jinming Cao, Honglin Lin, Zhihong Lu, Hao Jin, Jian Wang, Lijun Wu
Neurons and Cognition
Rethinking World Models for Biosignals
Xianglong Bao, Bosong Huang, Xiaoda Wang et al. (+4 more)
World models promise to learn how systems evolve and respond to actions, enabling simulation, planning and decision-making. Bringing this promise to biosignals requires more than accurate prediction of physiological recordings. We examine existing biosignal world models through two requirements for intervention use: a genuine action and evidence of its causal effect. We identify three fundamental bottlenecks rooted in physiology: Temporal Latency, the variable delay between an action and its observable effect; System Openness, the influence of drivers beyond the modelled intervention; and Endogenous Dynamics, the system's ongoing
evolution without that intervention. Drawing on published evidence, we outline corresponding research directions and propose an Intervention-Effect Validation Checklist to guide evaluation. The aim is to help the biosignal community realise the core promise of world models by learning the latent mechanisms that govern physiological
evolution and responses to interventions.
Read the full abstract ▾
All authors Xianglong Bao, Bosong Huang, Xiaoda Wang, Keqi Han, Carl Yang, Ming Jin, Shirui Pan
Neurons and Cognition · Cross-listed from physics.soc-ph
Collective Benefits and Efficiency in Coupled Echo State Networks
Polyphony Bruna, Rick Dale, Michael J. Spivey et al. (+1 more)
Simple aggregation of independent responses ("wisdom of crowds") generally improves collective performance through statistical facilitation, but interaction among members can sometimes hinder group outcomes. However, under certain circumstances, coordination studies show that interacting groups can outperform their individual members and nominal groups, and interaction can also be more efficient than independence. We present a reservoir computing model to investigate how interaction can enhance joint performance in terms of accuracy and efficiency. Echo state networks were trained independently to identify Japanese speakers from vowel recordings and tested jointly with and without coupled feedback. Model pairs reproduced the dyadic advantage reported by Bahrami et al. (2010) with interaction via pooled-feedback, but not when independent responses were aggregated together. Model dynamics revealed that interaction was beneficial whereas aggregated outputs were prone to diverging from runaway feedback over time. In terms of efficiency, larger groups of smaller interacting models outperformed smaller groups of larger models while equating for total amount of group computation. Our results offer a formal framework for explaining when and why interaction enhances collective intelligence.
Read the full abstract ▾
All authors Polyphony Bruna, Rick Dale, Michael J. Spivey, Christopher T. Kello