The Recurrent Temporal Restricted Boltzmann Machine Captures Neural Assembly Dynamics in Whole-brain Activity

• Published in: bioRxiv
• Main Authors: Sebastian Quiroz Monnens, Peters, Casper, Hesselink, Luuk Willem, Smeets, Kasper, Englitz, Bernhard
• Format: Paper
• Language: English
• Published: Cold Spring Harbor Cold Spring Harbor Laboratory Press 04.02.2024
• Subjects: Danio rerio; Exploratory behavior; Stochasticity; Transfer learning
• DOI: 10.1101/2024.02.02.578570

Animal behaviour alternates between stochastic exploration and goal-directed actions, which are generated by the underlying neural dynamics. Previously, we demonstrated that the compositional Restricted Boltzmann Machine (cRBM) can decompose whole-brain activity of larval zebrafish data at the neural level into a small number ($\sim$100-200) of assemblies that can account for the stochasticity of the neural activity (van der Plas et al., eLife, 2023). Here we advance this representation by extending to a combined stochastic-dynamical representation to account for both aspects using the Recurrent Temporal RBM (RTRBM) and transfer-learning based on the cRBM estimate. We demonstrate that the functional advantage of the RTRBM is captured in the temporal weights on the hidden units, representing neural assemblies, both in simulated and experimental data. Our results show that the temporal expansion outperforms the stochastic-only cRBM in terms of generalization error and achieves more accurate representation of the moments in time. Lastly, we demonstrate that we can identify the original time-scale of assembly dynamics, by estimating multiple RTRBMs at different temporal resolutions. Together, we propose that RTRBMs are a valuable tool for capturing the combined stochastic and time-predictive dynamics of large-scale data sets.Competing Interest StatementThe authors have declared no competing interest.Footnotes* https://gin.g-node.org/vdplasthijs/cRBM_zebrafish_spontaneous_data