Real-time sensory–motor integration of hippocampal place cell replay and prefrontal sequence learning in simulated and physical rat robots for novel path optimization

Abstract

An open problem in the cognitive dimensions of navigation concerns how previous exploratory experience is reorganized in order to allow the creation of novel efficient navigation trajectories. This behavior is revealed in the “traveling salesrat problem” (TSP) when rats discover the shortest path linking baited food wells after a few exploratory traversals. We have recently published a model of navigation sequence learning, where sharp wave ripple replay of hippocampal place cells transmit “snippets” of the recent trajectories that the animal has explored to the prefrontal cortex (PFC) (Cazin et al. in PLoS Comput Biol 15:e1006624, 2019). PFC is modeled as a recurrent reservoir network that is able to assemble these snippets into the efficient sequence (trajectory of spatial locations coded by place cell activation). The model of hippocampal replay generates a distribution of snippets as a function of their proximity to a reward, thus implementing a form of spatial credit assignment that solves the TSP task. The integrative PFC reservoir reconstructs the efficient TSP sequence based on exposure to this distribution of snippets that favors paths that are most proximal to rewards. While this demonstrates the theoretical feasibility of the PFC–HIPP interaction, the integration of such a dynamic system into a real-time sensory–motor system remains a challenge. In the current research, we test the hypothesis that the PFC reservoir model can operate in a real-time sensory–motor loop. Thus, the main goal of the paper is to validate the model in simulated and real robot scenarios. Place cell activation encoding the current position of the simulated and physical rat robot feeds the PFC reservoir which generates the successor place cell activation that represents the next step in the reproduced sequence in the readout. This is input to the robot, which advances to the coded location and then generates de novo the current place cell activation. This allows demonstration of the crucial role of embodiment. If the spatial code readout from PFC is played back directly into PFC, error can accumulate, and the system can diverge from desired trajectories. This required a spatial filter to decode the PFC code to a location and then recode a new place cell code for that location. In the robot, the place cell vector output of PFC is used to physically displace the robot and then generate a new place cell coded input to the PFC, replacing part of the software recoding procedure that was required otherwise. We demonstrate how this integrated sensory–motor system can learn simple navigation sequences and then, importantly, how it can synthesize novel efficient sequences based on prior experience, as previously demonstrated (Cazin et al. 2019). This contributes to the understanding of hippocampal replay in novel navigation sequence formation and the important role of embodiment.

Appendix

Code for the HIPP–PFC model simulator is available at: https://github.com/NicolasCAZIN/TRN. Code for the animat SCS simulator integrated with the model is available at: https://github.com/biorobaw/scs.