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Poster C93 in Poster Session C - Friday, August 9, 2024, 11:15 am – 1:15 pm, Johnson Ice Rink
Paradoxical replay maintains unbiased and robust representations of task structure
Hung-Tu Chen1 (), Matthijs van der Meer1; 1Dartmouth College
Experience replay is a powerful mechanism to learn efficiently from limited experience. Despite several decades of striking experimental results, the factors that determine which experiences are selected for replay remain unclear. A particular challenge for current theories is that on tasks that feature unbalanced experience, rats paradoxically replay the less-experienced trajectory. To understand why, we simulated a feedforward neural network with two regimes: rich learning (structured representations tailored to task demands) and lazy learning (unstructured, task-agnostic representations). We find that rich, but not lazy, representations degrade following unbalanced experience, an effect that could be reversed with paradoxical replay. To test if this computational principle can account for the experimental data, we examined the relationship between paradoxical replay and learned task representations in the hippocampus. Strikingly, we find a strong association between the richness of learned task representations and the paradoxicality of replay. Taken together, these results suggest that paradoxical replay specifically serves to protect rich representations from the the destructive effects of unbalanced experience.
Keywords: neural network models replay hippocampus rich and lazy learning