BunDLe-Net: Neuronal Manifold Learning Meets Behaviour
- Author(s)
- Akshey Kumar, Aditya Gilra, Mauricio Gonzalez Soto, Anja Meunier, Moritz Grosse-Wentrup
- Abstract
Neuronal manifold learning techniques represent high-dimensional neuronal dynamics in low-dimensional embeddings to reveal the intrinsic structure of neuronal manifolds. Common to these techniques is their goal to learn low-dimensional embeddings that preserve all dynamic information in the high-dimensional neuronal data, i.e., embeddings that allow for reconstructing 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 preserves 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 to decoding behaviour, effectively de-noising the data to reveal better the intricate relationships between neuronal dynamics and behaviour. We demonstrate the quantitative superiority of BunDLe-Net over commonly used and state-of-the-art neuronal manifold learning techniques in terms of dynamic and behavioural information in the learned manifold on calcium imaging data recorded in the nematode C. elegans. Qualitatively, we show that BunDLe-Net learns highly consistent manifolds across multiple worms that reveal the neuronal and behavioural motifs that form the building blocks of the neuronal manifold.
- Organisation(s)
- Research Group Neuroinformatics, Vienna Cognitive Science Hub, Research Network Data Science
- Pages
- 1
- No. of pages
- 23
- DOI
- https://doi.org/10.1101/2023.08.08.551978
- Publication date
- 10-2023
- Austrian Fields of Science 2012
- 102001 Artificial intelligence, 106025 Neurobiology
- Portal url
- https://ucris.univie.ac.at/portal/en/publications/bundlenet-neuronal-manifold-learning-meets-behaviour(aa4fbdfd-dae5-459e-9645-dfadd783ab70).html