Hippocampal replay contributes to within session learning in a temporal difference reinforcement learning model

By Adam Johnson, A. David Redish. 2005

102 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

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Citations in the corpus

Offline replay supports planning in human reinforcement learning (2018, background): “While the rodent literature has shown in various paradigms that offline replay is associated with better task performance (Johnson and Redish, 2005; Ólafsdóttir et al., 2015; Ambrose et al., 2016a), none verifies that these improvements are specifically due to the offline integration of DOI:…”
Rewarded Outcomes Enhance Reactivation of Experience in the Hippocampus (2009, unknown): “Thus, because SWR reactivation can occur after traversing a path, it could allow the animal to learn the relationship between the path and its outcome (Johnson and Redish, 2005; Foster and Wilson, 2006; Diba and Buzsáki, 2007).”
Integrating memories to guide decisions (2015, result): “Applied to biological learning [46], this mechanism might help explain why modelbased and model-free learning seem to share more neural substrates — such as dopamine and reward prediction errors — than previously expected [47–49].”
Goals and Habits in the Brain (2013, unknown): “One computational formulation that addresses this question is DYNA-Q (Sutton, 1990), which allows an agent to exploit previously recorded experience to update values and policies, an idea that has now been exploited in modeling studies (Johnson and Redish, 2005).”
Prioritized memory access explains planning and hippocampal replay (2017, methodology): “…as planning (Johnson and Redish, 2007; Pfeiffer and Foster, 2013), learning through credit assignment (Ambrose et al., 2016; Foster and Wilson, 2006; Johnson and Redish, 2005), memory retrieval (Jadhav et al., 2012) and consolidation (Carr et al., 2011; McClelland et al., 1995), and in forming and…”