Selective Memory Replay Improves Exploration in a Spiking Wavefront Planner.

Spiking wavefront planners for navigation demonstrate biologically plausible behavior when exploring and planning paths through an environment. Not present in these models, however, is the replay of previous experiences observed in hippocampal sharp wave ripple complexes (SWRs) during sleep and wake resting states. This work implements a memory replay algorithm in a spiking wavefront model, and investigates different theories of replay selection. Results indicate that the addition of replay in the spiking wavefront model improves the speed at which the agent learns the environment, and the ability to adapt to change. Furthermore, selection of replays based on its effectiveness in updating model weights leads to greater improvement when compared to a uniformly weighted selection.