# ZAPBench: A Benchmark for Whole-Brain Activity Prediction in Zebrafish

arXiv: https://arxiv.org/abs/2503.02618 · HTML: https://mlsanity.com/paper/2503.02618/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 64 references that are in the map.
- First posted on arXiv 4 Mar 2025.

## Builds on (most cited first, 16 of 16)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://mlsanity.com/paper/2211.14730/) (2022)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [Are Transformers Effective for Time Series Forecasting?](https://mlsanity.com/paper/2205.13504/) (2022)
- [Long-term Forecasting with TiDE: Time-series Dense Encoder](https://mlsanity.com/paper/2304.08424/) (2023)
- [WeatherBench: A benchmark dataset for data-driven weather forecasting](https://mlsanity.com/paper/2002.00469/) (2020)
- [TSMixer: An All-MLP Architecture for Time Series Forecasting](https://mlsanity.com/paper/2303.06053/) (2023)
- [WeatherBench 2: A benchmark for the next generation of data-driven global weather models](https://mlsanity.com/paper/2308.15560/) (2023)
- [Deep Learning for Day Forecasts from Sparse Observations](https://mlsanity.com/paper/2306.06079/) (2023)
- [Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity](https://mlsanity.com/paper/2109.04463/) (2021)
- [The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos](https://mlsanity.com/paper/2305.19654/) (2023)
- [Forecasting Whole-Brain Neuronal Activity from Volumetric Video](https://mlsanity.com/paper/2503.00073/) (2025)
- [Low-dimensional functionality of complex network dynamics: Neuro-sensory integration in the Caenorhabditis elegans connectome](https://mlsanity.com/paper/1310.6689/) (2013)

## Built on by (most cited first, 10 of 10)

- [Structured Scaling of AI Discovery Across Diverse Scientific Domains](https://mlsanity.com/paper/2604.19341/) (2026)
- [POCO: Scalable Neural Forecasting through Population Conditioning](https://mlsanity.com/paper/2506.14957/) (2025)
- [Aster: Autonomous Scientific Discovery over 20x Faster Than Existing Methods](https://mlsanity.com/paper/2602.07040/) (2026)
- [Neuroprobe: Evaluating Intracranial Brain Responses to Naturalistic Stimuli](https://mlsanity.com/paper/2509.21671/) (2025)
- [Benchmarking Probabilistic Time Series Forecasting Models on Neural Activity](https://mlsanity.com/paper/2510.18037/) (2025)
- [NLP4Neuro: Sequence-to-sequence learning for neural population decoding](https://mlsanity.com/paper/2507.02264/) (2025)
- [Discovering Mechanistic Models of Neural Activity: System Identification in an in Silico Zebrafish](https://mlsanity.com/paper/2602.04492/) (2026)
- [Graph neural networks uncover structure and functions underlying the activity of simulated neural assemblies](https://mlsanity.com/paper/2602.13325/) (2026)
- [Probabilistic Prediction of Neural Dynamics via Autoregressive Flow Matching](https://mlsanity.com/paper/2604.11178/) (2026)
- [Integrative neurocybernetic modeling in the era of large-scale neuroscience](https://mlsanity.com/paper/2604.23903/) (2026)

## Abstract (arXiv)

Data-driven benchmarks have led to significant progress in key scientific modeling domains including weather and structural biology. Here, we introduce the Zebrafish Activity Prediction Benchmark (ZAPBench) to measure 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 over 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 forecasting methods.