Samia Mohinta, Rui Ponte Costa, Stephane Ciocchi

25 Sep 2019 (modified: 24 Dec 2019)ICLR 2020 Conference Blind SubmissionReaders: Everyone
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  • Abstract: The hippocampus has long been associated with spatial memory and goal-directed spatial navigation. However, the region’s independent role in continual learning of navigational strategies has seldom been investigated. Here we analyse populationlevel activity of hippocampal CA1 neurons in the context of continual learning of two different spatial navigation strategies. Demixed Principal Component Analysis (dPCA) is applied on neuronal recordings from 612 hippocampal CA1 neurons of rodents learning to perform allocentric and egocentric spatial tasks. The components uncovered using dPCA from the firing activity reveal that hippocampal neurons encode relevant task variables such decisions, navigational strategies and reward location. We compare this hippocampal features with standard reinforcement learning algorithms, highlighting similarities and differences. Finally, we demonstrate that a standard deep reinforcement learning model achieves similar average performance when compared to animal learning, but fails to mimic animals during task switching. Overall, our results gives insights into how the hippocampus solves reinforced spatial continual learning, and puts forward a framework to explicitly compare biological and machine learning during spatial continual learning.
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