BunDLe-Net: Neuronal Manifold Learning Meets Behaviour

Abstract Neuronal manifold learning techniques represent high-dimensional neuronal dynamics in low-dimensional embeddings to reveal the intrinsic structure of neuronal manifolds. A common goal of these techniques is to learn embeddings that allow a good reconstruction of the original data. We introduce a novel neuronal manifold learning technique, BunDLe-Net, that learns a low-dimensional Markovian embedding of the neuronal dynamics which pre-serves only those aspects of the neuronal dynamics that are relevant for a given behavioural context. In this way, BunDLe-Net eliminates neuronal dynamics that are irrelevant for decoding behaviour, effectively de-noising the data to reveal better the intricate relationships between neuronal dynamics and behaviour. We show that BunDLe-Net learns highly consistent manifolds across animals that reveal the building blocks of their neuronal manifolds on a variety of data sets, ranging from calcium imaging data recorded in the nematode C. elegans to spiking data from the rat hippocampus and primate somatosensory cortex.