google Improving brain models with ZAPBench (opens in new tab)
Google Research, in collaboration with HHMI Janelia and Harvard, has introduced ZAPBench, a first-of-its-kind whole-brain activity dataset and benchmark designed to improve the accuracy of brain activity models. Using the larval zebrafish as a model organism, the project provides single-cell resolution recordings of approximately 70,000 neurons, capturing nearly the entire vertebrate brain in action. This resource allows researchers to bridge the gap between structural connectomics and dynamic functional activity to better understand how neural wiring generates complex behavior. ## Whole-Brain Activity in Larval Zebrafish * The dataset focuses on the six-day-old larval zebrafish because it is small, transparent, and capable of complex behaviors like motor learning, hunting, and memory. * Researchers used light-sheet microscopy to scan the brain in 3D slices, recording two hours of continuous activity. * The fish were engineered with GCaMP, a genetically encoded calcium indicator that emits light when neurons fire, allowing for the visualization of real-time neural impulses. * To correlate neural activity with behavior, the fish were placed in a virtual reality environment where stimuli—such as shifting water currents and light changes—were projected around them while tail muscle activity was recorded via electrodes. ## The ZAPBench Framework * ZAPBench standardizes the evaluation of machine learning models in neuroscience, following the tradition of benchmarks in fields like computer vision and language modeling. * The benchmark provides a high-quality dataset of 70,000 neurons, whereas previous efforts in other species often covered less than 0.1% of the brain. * It challenges models to predict how neurons will respond to specific visual stimuli and behavioral patterns. * Initial results presented at ICLR 2025 demonstrate that while simple linear models provide a baseline, advanced architectures like Transformers and Convolutional Neural Networks (CNNs) significantly improve prediction accuracy. ## Integrating Structure and Function * While previous connectomics projects mapped physical neural connections, ZAPBench adds the "dynamic" layer of how those connections are used over time. * The team is currently generating a comprehensive structural connectome for the exact same specimen used in the activity recordings. * This dual approach will eventually allow scientists to investigate the direct relationship between precise physical wiring and the resulting patterns of neural activity across an entire vertebrate brain. By providing an open-source dataset and standardized benchmark, ZAPBench enables the global research community to develop and compare more sophisticated models of neural dynamics, potentially leading to breakthroughs in how we simulate and understand vertebrate cognition.