CytoNet is a foundation model trained on 1 million unlabeled microscopic image patches from over 4,000 histological sections of nine postmortem brains, and evaluated on 2,000 sections from five additional brains. By using co‑localization in the cortical sheet for self‑supervision, it learns expressive, anatomically meaningful feature representations that enable downstream tasks such as area classification, laminar segmentation, microarchitectural quantification, and exploratory mapping of cortical subdivisions. Functional parcellation analyses demonstrate links between cytoarchitecture and macroscale functional organization, establishing CytoNet as a unified framework for scalable analysis of cortical microarchitecture and its relationship to structure‑function organization in the human cerebral cortex.
arXiv:2607. 17782v1 Announce Type: cross Abstract: Foundation models pretrained using self-supervised learning have transformed computer vision by learning transferable representations from large-scale unlabeled data.
arXiv:2606. 09770v1 Announce Type: cross Abstract: Nearby neurons in cortex share similar response profiles, producing systematic spatial organization across sensory and cognitive systems.
arXiv:2606. 04772v1 Announce Type: cross Abstract: Understanding the relationship between deep visual representations and the human visual system is a fundamental challenge in computational neuroscience.
arXiv:2608. 09636v1 Announce Type: cross Abstract: Accurate 3D neuron segmentation in fluorescence microscopy is critical for neuroscience.
arXiv:2604. 27277v3 Announce Type: replace-cross Abstract: Brain MRI underpins a wide range of neuroscientific and clinical applications, yet most learning-based methods remain task-specific and require substantial labeled data.
Understanding the relationship between deep visual representations and the human visual system is a fundamental challenge in computational neuroscience. While modern vision models achieve strong performance in image recognition, their correspondence with the hierarchical organization of the human visual cortex remains an open question.
arXiv:2606. 05870v1 Announce Type: cross Abstract: Neurodegenerative disorders such as Alzheimer's disease exhibit highly organized patterns of regional brain vulnerability, yet the biological mechanisms underlying this spatial selectivity remain incompletely understood.
arXiv:2609.31204v1 Announce Type: cross Abstract: Recent fMRI foundation models differ substantially in the spatial scale at which they represent brain activity. ROI- and connectivity-based models ar...
arXiv:2503. 13212v3 Announce Type: replace Abstract: Alignment between human brain networks and artificial models has become an active research area in vision science and machine learning.
The paper investigates how data scaling should be viewed as an allocation problem rather than a simple sample count, focusing on spatially structured data from human brain histology. By separating unique sample count, source diversity, and spatial coverage, the authors conduct 93 pretraining runs on 11.6 million image patches from 21 brains, showing that performance improves with more unique samples, broader spatial coverage, more compute, and larger models. However, at a fixed sample budget, distributing samples across multiple subjects does not yield additional benefit, indicating that inter‑subject variation impacts generalization but adding more sources does not help when the sample count is held constant.
The paper investigates combining a domain‑specific self‑supervised task—voxel‑level brain age prediction—with a general task—image inpainting—to pretrain models for brain MRI segmentation. A multitask pretraining framework jointly optimizes both objectives, yielding representations that outperform single‑task pretraining and training from scratch on three segmentation benchmarks (multiple sclerosis lesions, ischemic stroke lesions, and cortical structures). The study demonstrates that integrating domain‑specific and general self‑supervised tasks benefits the development of generalizable neuroimaging foundation models.
arXiv:2607. 19973v1 Announce Type: new Abstract: AI researchers describe state-of-the-art models as one thing repeated at scale: the Transformer, wired identically for text, pixels, or speech.