Hippocampal replay of extended experience

By T. J. Davidson, Fabian Kloosterman, Matthew A. Wilson. 2009

792 citations recorded by the source corpus.

Summary During pauses in exploration, ensembles of place cells in the rat hippocampus re-express firing sequences corresponding to recent spatial experience. Such ‘replay’ co-occurs with ripple events: short-lasting (~50–120 ms), high frequency (~200 Hz) oscillations that are associated with increased hippocampal-cortical communication. In previous studies, rats explored small environments, and replay was found to be anchored to the rat’s current location, and compressed in time such that replay of the complete environment occurred during a single ripple event. It is not known whether or how longer behavioral sequences are replayed in the hippocampus. Here we show, using a neural decoding approach, that firing sequences corresponding to long runs through a large environment are replayed with high fidelity (in both forward and reverse order), and that such replay can begin at remote locations on the track. Extended replay proceeds at a characteristic virtual speed of ~8 m/s, and remains coherent across trains of ripple events. These results suggest that extended replay is composed of chains of shorter subsequences, which may reflect a strategy for the storage and flexible expression of memories of prolonged experience.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Neuroscience

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Citations in the corpus

A Comprehensive Survey of Continual Learning: Theory, Method and Application (2023, background): “2 seconds) [52], [84], [227], which potentially improves resource efficiency.”
Memory Replay with Data Compression for Continual Learning (2022, positive): “Different from “artificial” memory replay in DNNs, a significant feature of biological memory is to encode the old experiences in a highly compressed form and replay them to overcome catastrophic forgetting (McClelland, 2013; Davidson et al., 2009; Carr et al., 2011).”
Replay in Deep Learning: Current Approaches and Missing Biological Elements (2021, positive): “Such replay of recent learning has been replicated across several studies (Louie and Wilson, 2001; Davidson, Kloosterman, & Wilson, 2009) and in other brain regions (Peyrache, Khamassi, Benchenane, Wiener, & Battaglia, 2009; Ji & Wilson, 2007).”
Hippocampal replay in the awake state: a potential physiological substrate of memory consolidation and retrieval (2011, background): “Examination of hippocampal activity during exploration of a larger environment revealed that both forward and reverse replay events can span long distances and extend across multiple SWR”
Hippocampal Replay is Not a Simple Function of Experience (2010, background): “Whereas previous studies (Wil- son and McNaughton, 1994; Jackson et al., 2006; O’Neill et al., 2006; Karlsson and Frank, 2009) have found increasing replay with experience, these studies did not compare tasks with different behavioral requirements in which parts of the environment were experienced m”
Hippocampal place cells construct reward related sequences through unexplored space (2015, methodology): “Finally, to corroborate the results obtained from the rank-order analysis of spike sequences, we applied a Bayesian spatial reconstruction algorithm (Davidson et al., 2009; Bendor and Wilson, 2012) to the data from the two rest sessions.”
About sleep's role in memory. (2013, background): “In larger experimental environments, both forward and reverse replay can occur over multiple SW-R events thereby covering longer distances (268).”
Distinct hippocampal-cortical memory representations for experiences associated with movement versus immobility (2017, positive): “Trajectory replay events are biased to start from the animal’s current location (Davidson et al., 2009; Karlsson and Frank, 2009), which is often where the animal is immobile.”
Neural syntax: cell assemblies, synapsembles and readers (2010, positive): “…assembly replay during rest and sleep occurs in snippets and is faster, often compressed by at least a factor of ten compared to the behavioral timescale of neuronal activation (Davidson et al., 2009; Diba andBuzsáki, 2007; Euston et al., 2007; Foster and Wilson, 2006; Nádasdy et al., 1999).”
Machine Learning for Neural Decoding (2017, background): “, 2003; Ibos and Freedman, 2017), and spatial locations based on activity in the hippocampus (Zhang et al., 1998; Davidson et al., 2009).”
Rewarded Outcomes Enhance Reactivation of Experience in the Hippocampus (2009, positive): “In particular, reactivation during pauses in waking behavior frequently results in the sequential activity of place cells active on paths to or from the animal’s current location (Foster and Wilson, 2006; Diba and Buzsáki, 2007; Karlsson and Frank, 2009; Davidson et al., 2009).”
The minority report: some common assumptions to reconsider in the modelling of the brain and behaviour (2016, background): “…feat was made possible by ‘harnessing state-of-the-art machine learning techniques with biologically inspired mechanisms’, namely, experience replay (Davidson, Kloosterman, & Wilson, 2009) and a sparse (as opposed to continuous) updating of the action values, corresponding to infrequent reward.”
Hippocampal Reactivation of Random Trajectories Resembling Brownian Diffusion. (2019, background): “During exploration, the reactivation seen in transient SWRassociated network bursts tends to occur when animals stop briefly (Davidson et al., 2009; Foster andWilson, 2006).”
Sparse Attentive Backtracking: Temporal CreditAssignment Through Reminding (2018, background): “…recordings in rats indicate that brief sequences of prior experience are replayed both in the awake resting state and during sleep, both of which conditions are linked to memory consolidation and learning (Foster & Wilson, 2006; Davidson et al., 2009; Gupta et al., 2010; Ambrose et al., 2016).”