ePoster · Computational Neuroscience
Reverse engineering recurrent network models reveals mechanisms for location memory
Ian Hawes, Matt Nolan · Bernstein Conference 2024
What circuit mechanisms underlie the encoding and retrieval of spatial memory? In standard models, neurons have discrete fields that map onto locations, which are associated with actions [1]. Given that spatial-memory regions (such as hippocampus and entorhinal cortex) have recurrent architectures, it is unclear if this feedforward association mechanism is the only way in which spatial memories can be stored. Here, we propose a new mechanism for spatial memory that is derived from reverse-engineering recurrent neural networks (RNNs). We train RNNs on a spatial navigation task posed as a rein