Aligned and oblique dynamics in recurrent neural networks

Author:

Schuessler Friedrich12,Mastrogiuseppe Francesca3,Ostojic Srdjan4,Barak Omri5

Affiliation:

1. Faculty of Electrical Engineering and Computer Science, Technical University Berlin

2. Science of Intel ligence, Research Cluster of Excel lence

3. Champalimaud Research

4. Laboratoire de Neurosciences Cognitives et Computationnel les, INSERM U960, Ecole Normale Superieure - PSL Research University

5. Rappaport Faculty of Medicine and Network Biology Research laboratories, Technion - Israeli Institute of Technology

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 RNNs can operate in two regimes: dynamics can either be aligned with the directions that generate output variables, or oblique to them. We show that the magnitude of the readout weights can serve as a control knob between the regimes. Importantly, these regimes are functionally distinct. Oblique networks are more heterogeneous and suppress noise in their output directions. They are furthermore more robust to perturbations along the output directions. Finally, we show that the two regimes can be dissociated in neural recordings. Altogether, our results open a new perspective for interpreting neural activity by relating network dynamics and their output.

Publisher

eLife Sciences Publications, Ltd

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