title: HIPPOCAMPAL NEURONAL REPRESENTATIONS IN CONTINUAL LEARNING
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.
authors: Samia Mohinta Rui Ponte Costa Stephane Ciocchi
hIndex: Rui Ponte Costa 13