Fishbank: A Zebrafish Benchmark for Cross-Individual Whole-Brain Activity Prediction

Forecasting whole-brain neural activity provides a quantitative test of brain dynamics models. We investigate the predictability of future activity from recent observations across spatial and temporal scales, individuals, and stimulus conditions. To support this investigation, we introduce FishBank, a multi-individual zebrafish forecasting benchmark comprising calcium imaging recordings of 189 zebrafish spanning three broad sensory categories. Spatial realignment, blockwise aggregation, and activity standardization transform heterogeneous recordings into common block-level activity sequences without requiring one-to-one neuronal correspondence, enabling systematic evaluation across model families, spatiotemporal settings, and training-data compositions. Our evaluation reveals three main findings. First, all learned predictors outperform naive baselines, while five deep neural networks performs closely. Second, forecasting performance depends on the spatial and temporal definition of the target. Third, multi-individual training yields remarkably lower error than the average error of single-recording models, whereas mixed-stimulus settings achieve slight improvement. FishBank provides a unified framework for characterizing the predictability of whole-brain activity, its spatiotemporal dependence, and the benefits and limitations of learning across individuals. We will publicly release FishBank, together with the associated preprocessing and benchmarking code, to facilitate future research.