Experience-Driven Rate Modulation is Reinstated During Hippocampal Replay

Abstract

Replay, the sequential reactivation of a neuronal ensemble, is thought to play a central role in the hippocampus during the consolidation of a recent experience into a long-term memory. Following a contextual change (e.g. entering a novel environment), hippocampal place cells typically modulate their in-field firing rate and shift the position of their place field, providing a rate and place representation for the behavioral episode, respectively. However, 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 the replay of two different contexts, mirroring the same pattern of rate modulation observed during behavior. This context-driven rate modulation within replay events was experience-dependent, observable during both behavioral episodes and throughout the subsequent rest period, but not prior to experience. Furthermore, we demonstrate that both the temporal order and firing rate of place cells can independently be used to decode contextual information within a replay event, revealing the existence of two separable but complementary neural representations available for memory consolidation processes.

Evaluating hippocampal replay without a ground truth

“…For both POST and RUN, the mean log odds difference increased (higher trajectory discriminability) as the p-value threshold decreased, which also corresponded to a decrease in the number of detected events. In addition, we observed RUN replay events to be comparatively more prevalent and yielding a higher trajectory discriminability than POST replay events, in line with previous reports (Karlsson and Frank, 2009; Tirole & Huelin Gorriz, et al, 2022) . This might be partly due to the presence of immediate sensory or other external inputs helping to direct hippocampal place cell ensembles towards representing the local current environment during awake replay.…”

“…We next created a framework for evaluating and comparing replay detection methods, using a comparison between a replay event’s sequence fidelity and its trajectory discriminability. For each replay event detected, we used a sequenceless, log odds metric based on only place cells with place fields on both tracks , to avoid any potential bias in track discrimination ( Figure 1D-E see Methods) (Carey et al, 2019; Tirole & Huelin Gorriz, et al, 2022) . Sequenceless decoding involved three steps: 1) computing the summed posteriors (across time bins) within the replay event for each track, 2) calculating the ratio between the log of the summed posteriors for each track, and 3) taking the z-score of this value, based on a distribution of log odds computed by a track ID shuffle (Carey et al, 2019; Tirole & Huelin Gorriz, et al, 2022) .…”

“…For each replay event detected, we used a sequenceless, log odds metric based on only place cells with place fields on both tracks , to avoid any potential bias in track discrimination ( Figure 1D-E see Methods) (Carey et al, 2019; Tirole & Huelin Gorriz, et al, 2022) . Sequenceless decoding involved three steps: 1) computing the summed posteriors (across time bins) within the replay event for each track, 2) calculating the ratio between the log of the summed posteriors for each track, and 3) taking the z-score of this value, based on a distribution of log odds computed by a track ID shuffle (Carey et al, 2019; Tirole & Huelin Gorriz, et al, 2022) . For each place cell in the track ID shuffle, the corresponding track 1 and 2 place fields were randomly assigned to the correct track or swapped.…”

“…All the behavioral and electrophysiological data used in this study were previously described in detail in Tirole & Huelin-Gorriz, et al (2022).…”

“…In line with these developments, the analysis methods for replay have also become more sophisticated - shifting from the pairwise analysis of place cells to detecting sequential patterns within neuronal ensembles, using either spiking activity directly (Foster and Wilson, 2006) or decoding this activity to extrapolate the virtual spatial trajectories replaying (Davidson et al, 2009; Zhang et al, 1998). Some of the most commonly used replay scoring metrics for quantifying the fidelity of a replay sequence include - 1) a Spearman’s rank-order correlation of spike times, which quantifies the ordinal relationship between the temporal order of place cell firing during behavior and a replay event’s spike train (Foster and Wilson, 2006) , but assumes that the place cell sequence is ordered accordingly to each place cell’s peak firing rate location alone, 2) a Weighted correlation of the decoded replay event, which quantifies a generalized linear correlation in time and position weighted by the decoded posterior probabilities without any assumption about the temporal rigidity of the replayed trajectory (Grosmark and Buzsaki, 2016; Silva et al, 2015; Tirole & Huelin Gorriz, et al, 2022) , and 3) a Linear fitting of the decoded replay event, which finds the linear path with the maximum summed decoded probability, assuming that the trajectory’s slope is constant (Davidson et al, 2009; Gomperts et al, 2015; Ólafsdóttir et al, 2017). However, because replay is generated by an internal and spontaneous state of the brain, there is no external reference to indicate whether a given replay event is truly a reinstatement of a memory trace.…”