Jan-Matthis Lueckmann
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ZAPBench: A Benchmark for Whole-Brain Activity Prediction in Zebrafish
Alexander Immer
Alex Bo-Yuan Chen
Mariela D. Petkova
Nirmala A. Iyer
Luuk Willem Hesselink
Aparna Dev
Gudrun Ihrke
Woohyun Park
Alyson Petruncio
Aubrey Weigel
Wyatt Korff
Florian Engert
Jeff W. Lichtman
Misha B. Ahrens
International Conference on Learning Representations (ICLR) (2025)
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Data-driven benchmarks have led to significant progress in key scientific modeling domains including weather and structural biology. Here, we present the Zebrafish Activity Prediction Benchmark (ZAPBench), which quantitatively measures progress on the problem of predicting cellular-resolution neural activity throughout an entire vertebrate brain. The benchmark is based on a novel dataset containing 4d light-sheet microscopy recordings of more than 70,000 neurons in a larval zebrafish brain, along with motion stabilized and voxel-level cell segmentations of these data that facilitate development of a variety of forecasting methods. Initial results from a selection of time series and volumetric video modeling approaches achieve better performance than naive baseline methods, but also show room for further improvement. The specific brain used in the activity recording is also undergoing synaptic-level anatomical mapping, which will enable future integration of detailed structural information into ZAP forecasting methods.
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Simulation-Based Inference: A Practical Guide
Michael Deistler
Jan Boelts
Peter Steinbach
Guy Moss
Thomas Moreau
Manuel Gloeckler
Pedro L. C. Rodriguez
Julia Linhart
Janne K. Lappalainen
Benjamin Kurt Miller
Pedro J. Goncalves
Cornelius Schröder
Jakob H. Macke
arXiv (2025)
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A central challenge in many areas of science and engineering is to identify model parameters that are consistent with empirical data and prior knowledge. Bayesian inference offers a principled framework for this task, but can be computationally prohibitive when models are defined by stochastic simulators. Simulation-Based Inference (SBI) provides a suite of methods to overcome this limitation and has enabled scientific discoveries in fields such as particle physics, astrophysics and neuroscience. The core idea of SBI is to train neural networks on data generated by a simulator, without requiring access to likelihood evaluations. Once trained, the neural network can rapidly perform inference on empirical observations without requiring additional optimization or simulations. In this tutorial, we provide a practical guide for practitioners aiming to apply SBI methods. We outline a structured SBI workflow and offer practical guidelines and diagnostic tools for every stage of the process--from setting up the simulator and prior, choosing the SBI method and neural network architecture, training the inference model, to validating results and interpreting the inferred parameters. We illustrate these steps through examples from astrophysics, psychophysics, and neuroscience. This tutorial empowers researchers to apply state-of-the-art SBI methods, facilitating efficient parameter inference for scientific discovery.
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