The use of animals in neuroscience research is a fundamental tool to understand the inner workings of the brain during perception and cognition in health and disease. Neuroscientists train animals (often rodents) in behavioral tasks over several months, however training protocols are sometimes not well defined and this leads to delays in research, additional costs, or the need of more animals. Finding strategies to optimize animal training in safe and ethical ways is therefore of crucial importance in neuroscience. In this project, we will develop a computational framework based on artificial neural networks (ANNs), to predict the behavioral output of animals trained in neuroscience tasks. We will use existing behavioral data to train our ANNs in the same way that animals are trained, and study whether our models can predict the behavior of the animals. Our results will lead to more efficient training protocols and improve neuroscience practices.