Aligned and oblique dynamics in recurrent neural networks.

ABSTRACT: The relation between neural activity and behaviorally relevant variables is at the heart of neuroscience research. When strong, this relation is termed a neural representation. There is increasing evidence, however, for partial dissociations between activity in an area and relevant external variables. While many explanations have been proposed, a theoretical framework for the relationship between external and internal variables is lacking. Here, we utilize recurrent neural networks (RNNs) to explore the question of when and how neural dynamics and the network's output are related from a geometrical point of view. We find that training RNNs can lead to two dynamical regimes: dynamics can either be aligned with the directions that generate output variables, or oblique to them. We show that t

SUBMITTER: Schuessler F 

PROVIDER: S-EPMC11610043 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature