A model-free approach to link neural activity to behavioral tasks

Abstract

SummaryBrains are not engineered solutions to a well-defined problem but arose through selective pressure acting on random variation. It is therefore unclear how well a model chosen by an experimenter can relate neural activity to experimental conditions. Here we developed “Model-free identification of neural encoding (MINE).” MINE is an accessible framework that uses convolutional neural networks (CNN) to relate aspects of tasks to neural activity and characterizes this relationship. Although flexible, CNNs are difficult to interpret. We use Taylor decomposition approaches to understand the mapping from task features to activity. We apply MINE to a published cortical dataset as well as experiments designed to probe thermoregulatory circuits in zebrafish. MINE allowed us to characterize neurons according to their receptive field and computational complexity, features which anatomically segregate in the brain. We also identified a new class of neurons that integrate thermosensory and behavioral information which eluded us previously when using traditional clustering and regression-based approaches.