2025/01/24 by Ros, Berta, Olives-Verger, Mireia, Fuses, Caterina +6
Biochemistry, Genetics and Molecular Biology · Materials Science · #Advanced Electron Microscopy Techniques and Applications #Cell Image Analysis Techniques #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Materials Science #Neurons and Cognition (q-bio.NC)
paper · pdf · doi:10.48550/arxiv.2501.14615
openalex publication_date 2025/01/24 · openalex created_date 2025/01/28 · openalex updated_date 2026/07/28
Calcium imaging allows for the parallel measurement of large neuronal populations in a spatially resolved and minimally invasive manner, and has become a gold-standard for neuronal functionality. While deep generative models have been successfully applied to study the activity of neuronal ensembles, their potential for learning single-neuron representations from calcium imaging fluorescence traces remains largely unexplored, and batch effects remain an important hurdle. To address this, we explore supervised variational autoencoder architectures that learn compact representations of individual neurons from fluorescent traces without relying on spike inference algorithms. We find that this approach outperforms state-of-the-art models, preserving biological variability while mitigating batch effects. Across simulated and experimental datasets, this framework enables robust visualization, clustering, and interpretation of single-neuron dynamics.