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2025-11-17
12:36
OpenAccess [FZJ-2025-04528] Preprint
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CytoNet: A Foundation Model for the Human Cerebral Cortex
arXiv () [10.48550/ARXIV.2511.01870]
To study how the human brain works, we need to explore the organization of the cerebral cortex and its detailed cellular architecture. We introduce CytoNet, a foundation model that encodes high-resolution microscopic image patches of the cerebral cortex into highly expressive feature representations, enabling comprehensive brain analyses. [...]
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2025-11-12
11:21

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2025-10-28
12:51

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2025-10-28
12:50

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2025-10-27
13:26

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2025-10-27
07:03

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2025-10-24
11:38
[FZJ-2025-04262] Preprint
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A flexible framework for structural plasticity in GPU-accelerated sparse spiking neural networks
arXiv () [10.48550/arXiv.2510.19764]
The majority of research in both training Artificial Neural Networks (ANNs) and modeling learning in biological brains focuses on synaptic plasticity, where learning equates to changing the strength of existing connections. However, in biological brains, structural plasticity - where new connections are created and others removed - is also vital, not only for effective learning but also for recovery from damage and optimal resource usage. [...]
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2025-10-20
12:24
[FZJ-2025-04217] Preprint
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acia-workflows: Automated Single-cell Imaging Analysis for Scalable and Deep Learning-based Live-cell Imaging Analysis Workflows
arXiv () [10.48550/ARXIV.2510.05886]
Live-cell imaging (LCI) technology enables the detailed spatio-temporal characterization of living cells at the single-cell level, which is critical for advancing research in the life sciences, from biomedical applications to bioprocessing. High-throughput setups with tens to hundreds of parallel cell cultivations offer the potential for robust and reproducible insights. [...]

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2025-10-20
12:20
[FZJ-2025-04215] Preprint
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PyUAT: Open-source Python framework for efficient and scalable cell tracking
arXiv () [10.48550/ARXIV.2503.21914]
Tracking individual cells in live-cell imaging provides fundamental insights, inevitable for studying causes and consequences of phenotypic heterogeneity, responses to changing environmental conditions or stressors. Microbial cell tracking, characterized by stochastic cell movements and frequent cell divisions, remains a challenging task when imaging frame rates must be limited to avoid counterfactual results. [...]

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2025-10-20
12:06
[FZJ-2025-04214] Preprint
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How To Make Your Cell Tracker Say 'I dunno!'
arXiv () [10.48550/ARXIV.2503.09244]
Cell tracking is a key computational task in live-cell microscopy, but fully automated analysis of high-throughput imaging requires reliable and, thus, uncertainty-aware data analysis tools, as the amount of data recorded within a single experiment exceeds what humans are able to overlook. We here propose and benchmark various methods to reason about and quantify uncertainty in linear assignment-based cell tracking algorithms. [...]

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