PT  - JOURNAL ARTICLE
AU  - Redman, William T.
AU  - Acosta, Francisco
AU  - Acosta–Mendoza, Santiago
AU  - Miolane, Nina
TI  - Not so griddy: Internal representations of RNNs path integrating more than one agent
AID  - 10.1101/2024.05.29.596500
DP  - 2024 Jan 01
TA  - bioRxiv
PG  - 2024.05.29.596500
4099  - http://biorxiv.org/content/early/2024/10/31/2024.05.29.596500.short
4100  - http://biorxiv.org/content/early/2024/10/31/2024.05.29.596500.full
AB  - 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, stronger border responses, and tuning for the relative position of the two agents. 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.Competing Interest StatementThe authors have declared no competing interest.