Abstract
For decades, studying the behavioral effects of individual drugs and genetic mutations has been at the heart of efforts to understand and treat nervous system disorders. High-throughput technologies adapted from other disciplines (e.g., high-throughput chemical screening, genomics) are changing the scale of data acquisition in behavioral neuroscience. Massive behavioral datasets are beginning to emerge, particularly from zebrafish labs, where behavioral assays can be performed rapidly and reproducibly in 96-well, high-throughput format. Mining these datasets and making comparisons across different assays are major challenges for the field. Here, we review behavioral barcoding, a process by which complex behavioral assays are reduced to a string of numeric features, facilitating analysis and comparison within and across datasets.