Not so griddy: Internal representations of RNNs path integrating more than one agent

William T Redman, Francisco Acosta, Santiago Acosta-Mendoza, Nina Miolane

Keywords: grid cells, path integration, recurrent neural networks, normative models

TL;DR: We study the representations that emerge in RNNs trained to path integrate two agents and compare them to the representations that emerge when path integrating a single agent.

Abstract: Success in collaborative and competitive environments, where agents must work with or against each other, requires individuals to encode the position and trajectory of themselves and others. Decades of neurophysiological experiments have shed light on how brain regions [e.g., medial entorhinal cortex (MEC), hippocampus] encode the self's position and trajectory. However, it has only recently been discovered that MEC and hippocampus are modulated by the positions and trajectories of others. To understand how encoding spatial information of multiple agents shapes neural representations, we train a recurrent neural network (RNN) model that captures properties of MEC to path integrate trajectories of two agents simultaneously navigating the same environment. We find significant differences between these RNNs and those trained to path integrate only a single agent. At the individual unit level, RNNs trained to path integrate more than one agent develop weaker grid responses and stronger border responses. At the population level, they develop more distributed and robust representations, with changes in network dynamics and manifold topology. Our results provide testable predictions and open new directions with which to study the neural computations supporting spatial navigation.

Submission Number: 61