The recurrent temporal restricted Boltzmann machine captures neural assembly dynamics in whole-brain activity.

ABSTRACT: 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 (∼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 ne

SUBMITTER: Quiroz Monnens S 

PROVIDER: S-EPMC11537485 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature