Experimental and Robotics Investigations of Multi-Scale Spatial Memory Consolidation in Complex Environments


Dates: 2017-2023

Principal Investigators

Alfredo Weitzenfeld (University of South Florida)
Jean-Marc Fellous (University of Arizona)

Main Participants

Pablo Scleidorovich (University of South Florida)
Chance Hamilton (University of South Florida)

Marco Contreras (University of Arizona)


Adaptive and robust intelligence rely on the ability to dynamically process information at multiple levels of granularity. Many studies suggest that spatial navigation is supported in large part by the multiscale neural system of the hippocampus. The dorsal hippocampus (DH) has long been known to have a critical role in spatial learning and memory, but most of the supporting experiments were conducted using simple spatial tasks in small arenas. Such conditions may not have required the integration of spatial representations at different scales along the dorsoventral axis of the hippocampus, and hence may not have relied on ventral hippocampal (VH) computations. Additionally, and mostly independently, recent investigations have also shown that hippocampal sharp wave ripples (SWR) measured in the dorsal hippocampus are implicated in spatial learning and memory consolidation during both awake and off-line (sleep) states. Cells have been shown to reactivate during these ripples with correlations patterns similar to that during learning. However, it remains unknown whether and how this replay occurs in the ventral hippocampus and whether it is important for spatial memory consolidation. Our overall hypothesis is that the interactions of spatial maps at multiple scales along the dorso-ventral axis of the hippocampus during sleep allow for the consolidation of spatial memory acquired in large and complex environments. The functional consequences of these interactions will be tested in a computational model controlling a mobile robot and we will show that such off-line processing will improve the performance of the robot during recall. We propose to use a goal-directed navigation task in a large environment with different obstacles densities together with extracellular electrophysiological recordings and optogenetic inactivation manipulations of the dorso-ventral axis of the hippocampus. We also propose to develop a novel computational model of dorso-ventral hippocampal replay during sleep, with applications to robotic navigation in complex environments.

Selected Bibliography

Scleidorovich, P., Fellous, J.M., Weitzenfeld, A., 2022, Adapting hippocampus multi-scale place field distributions in cluttered environments optimizes spatial navigation and learning, Frontiers in Computational Neuroscience, Dec.

Scleidorovich, P., Llofriu, M., Fellous, J.M., and Weitzenfeld, A., 2020, A Computational Model for Latent Learning based on Hippocampal Replay, IJCNN 2020, July 19-24, Glasgow, Scotland.

Scleidorovich, P., Llofriu, M., Fellous, J.M., Weitzenfeld, A., 2020, A Computational Model for Spatial Cognition Combining Dorsal and Ventral Hippocampal Place Field Maps: Multi-scale Navigation, Special Issue entitled ‘Complex Spatial Navigation in Animals, Computational Models and Neuro-inspired Robots’, Guest Editors P. Dominey, J.M. Fellous, and A. Weitzenfeld, Biological Cybernetics, https://doi.org/10.1007/s00422-019-00812-x

Llofriu, M., Scleidorovich, P., Tejera, G., Contreras, M., Pelc, T., Fellous, J.M., and Weitzenfeld, A., 2019, A Computational Model for a Multi-Goal Spatial Navigation Task inspired in Rodent Studies, IJCNN 2019, July 14-19, Budapest, Hungary, https://doi.org/10.1109/IJCNN.2019.8851852

Contreras, M., Pelc, T., Llofriu, M., Weitzenfeld, A. and Fellous, J.M., 2018, The ventral hippocampus is involved in multi-goal obstacle-rich spatial navigation, Hippocampus, Wiley, Vol. 28, No 12, pp 853-866, https://doi.org/10.1002/hipo.22993

Tejera, G., Llofriu, M., Barrera, A., and Weitzenfeld, A., 2018, Bio-Inspired Robotics: A Spatial Cognition Model integrating Place Cells, Grid Cells and Head Direction Cells, Journal of Intelligent and Robotic Systems, 91(1), 85-99, Springer, https://doi.org/10.1007/s10846-018-0852-2