Not So griddy: Internal Representations of RNNs Path Integrating More Than One Agent

Redman W., Acosta, F., Acosta-Mendoza, S., Miolane, N.

We show that RNNs trained to path integrate multiple agents develop distinct unit- and population-level representations - such as weaker grid cells, stronger border cells, and relative position tuning - highlighting how multi-agent navigation shapes spatial coding and neural dynamics.