Fast reverse replays of recent spatiotemporal trajectories in a robotic hippocampal model

A number of computational models have recently emerged in an attempt to understand the dynamics of hippocampal replay, but there has been little progress in testing and implementing these models in real-world robotics settings. Presented here is a bioinspired hippocampal CA3 network model, that runs in real-time to produce reverse replays of recent spatiotemporal sequences in a robotic spatial navigation task. For the sake of computational efficiency, the model is composed of continuous-rate based neurons, but incorporates two biophysical properties that have recently been hypothesised to play an important role in the generation of reverse replays: intrinsic plasticity and short-term plasticity. As this model only replays recently active trajectories, it does not directly address the functional properties of reverse replay, for instance in robotic learning tasks, but it does support further investigations into how reverse replays could contribute to functional improvements.