Brain-state invariant thalamo-cortical coordination revealed by non-linear encoders

Abstract

Understanding how neurons cooperate to integrate sensory inputs and guide behavior is a fundamental problem of neuroscience. A large body of methods have been developed to study neuronal firing at the single cell and population levels, generally seeking interpretability as well as predictivity. However, these methods are usually confronted with the lack of ground-truth necessary to validate the approach. Here, using neuronal data from the head-direction system, we present evidence how gradient boosted trees, a non-linear and supervised machine learning tool, learns the relationship between behavioral parameters and neuronal responses with high accuracy by optimizing the information rate. Interestingly, and unlike other classes of Machine Learning methods, the intrinsic structure of the trees can be interpreted in relation to behavior (e.g. to recover the tuning curves) or to study how neurons cooperate with their peers in the network. As an example, we show how the method reveals a temporally shifted coordination in a thalamo-cortical circuit during wakefulness and sleep, indicating a brain-state independent feed-forward circuit. Machine learning tools thus open new avenues for benchmarking model-based characterization of spike trains.