Experience-driven rate modulation is reinstated during hippocampal replay

Abstract

Replay, the sequential reactivation within a neuronal ensemble, is a central hippocampal mechanism postulated to drive memory processing. While both rate and place representations are used by hippocampal place cells to encode behavioral episodes, replay has been largely defined by only the latter – based on the fidelity of sequential activity across neighboring place fields. Here we show that dorsal CA1 place cells in rats can modulate their firing rate between replay events of two different contexts. This experience-dependent phenomenon mirrors the same pattern of rate modulation observed during behavior and can be used independently from place information within replay sequences to discriminate between contexts. Our results reveal the existence of two complementary neural representations available for memory processes.

The role of experience in prioritizing hippocampal replay

“…Because we were limited in the number of sessions that could be recorded from each animal, we used a fixed protocol with the rat always running more laps on track 1 compared to track 2, and with track 1 always being the first track used in the re-exposure. While it is possible that sleep replay rates increase with the recency of the behavioral episode to the sleep session (i.e., higher replay rates for track 2), previous replay data comparing replay between two novel tracks with a similar duration of experience suggests that this is not a significant effect 27 .…”

“…Using chronically implanted microdrives 25 – 27 , we recorded from large ensembles of place cells during our behavioral task and the corresponding sleep sessions. We observed that place cells globally remapped between tracks 27 , 28 , while having similar place fields on the same track between RUN1 (first exposure) and RUN2 (re-exposure) (Fig. 2A, B and Fig.…”

“…Using a naïve Bayes decoder, we reconstructed the rat’s position on each track from the firing rates of place cells during RUN1 and RUN2, and using this same approach we reconstructed the virtual spatial trajectories within replay events across the entire recording session. Candidate replay events were selected based on a minimum duration >= 100 ms, and a z -score threshold of 3 for the smoothed multi-unit activity across all channels 27 (see Methods). Candidate replay events with a z -score > 3 for ripple-band power, and a statistically significant weighted correlation score ( p < 0.05) for three different shuffle distributions 27 , 29 (see Methods) were considered significant replay events and included in our analysis (Fig.…”

“…Candidate replay events were selected based on a minimum duration >= 100 ms, and a z -score threshold of 3 for the smoothed multi-unit activity across all channels 27 (see Methods). Candidate replay events with a z -score > 3 for ripple-band power, and a statistically significant weighted correlation score ( p < 0.05) for three different shuffle distributions 27 , 29 (see Methods) were considered significant replay events and included in our analysis (Fig. 2C ).…”

“…Replay trajectories were decoded using a Bayesian decoding algorithm, as described in Tirole et al (2022) 27 . Detection of candidate sharp-wave ripple (SWR) associated replay events was based on the thresholds set on both multi-unit activity (MUA) and ripple-band power.…”