Hippocampal sharp waves and reactivation during awake states depend on repeated sequential experience

Abstract

Hippocampal firing patterns during behavior are reactivated during rest and subsequent slow-wave sleep. These reactivations occur during transient local field potential (LFP) events, termed sharp waves. Theories of hippocampal processing suggest that sharp waves arise from strengthened plasticity, and that the strengthened plasticity depends on repeated cofiring of pyramidal cells. We tested these predictions by recording neural ensembles and LFPs from rats running tasks requiring different levels of behavioral repetition. The number of sharp waves emitted increased during sessions with more regular behaviors. Reactivation became more similar to behavioral firing patterns across the session. This enhanced reactivation also depended on the regularity of the behavior. Additional studies in CA3 and CA1 found that the number of sharp waves emitted also increased in CA3 recordings as well as CA1, but that the time courses were different between the two structures.

Figure 1.

Example tetrode recordings: clustering, waveforms, and firing patterns. Spikes were clustered according to multiple waveform features including peak spike amplitude, energy, and principal components. Clustered spikes are shown with different colors for each cluster on two projections: peak spike amplitude on channel 1 versus channel 2, and peak amplitude on channel 3 versus channel 4. One millisecond waveforms for 7 of the 16 separable clusters are shown below color-coded by cluster color. ISI histograms from each neuron are typical of hippocampal pyramidal neurons. Calibration, 100 μV. ID, Isolation distance.

Figure 2.

Histology showing recording sites. The top panels show representative tetrode tracks in the CA3 (R057) and CA1 (R065) regions. The bottom panels show regions from which CA3 and CA1 recordings were taken.

Figure 3.

Methods: randomized controls for cell assembly similarity (diagrammatic). The top three rasters represent spikes from three simulated neurons. Time runs along the x-axis are shown. SWAP, The spikes are shuffled across neurons preserving their timing but changing the neuron they are assigned to. This preserves the overall ensemble firing patterns with respect to the oscillatory state shown at the top. SHUFF, The intervals between spikes are shuffled within the spike train of each neuron in the bottom rasters, preserving the firing statistics of each neuron but disrupting ensemble state-dependent temporal firing patterns.

Figure 4.

Experience-dependent changes in SW ripple events. Mean and SE of SW ripple events emission rate normalized by the time spent in nontheta were calculated from individual averages across animals. Linear regression line and 95% regression confidence intervals for all four conditions. ALL, Overall, including all three tasks, R² = 0.063, F = 14, p (slope = 0) < 0.0002; LT, R² = 0.098, F = 19, p (slope = 0) < 0.00002; CF, R² = 0.011, F = 2.3, p (slope = 0) > 0.12; CG, R² = 0.056, F = 10.3, p (slope = 0) < 0.002.

Figure 5.

SWR complexes during awake behavior include the same cell assemblies as occur during theta. The similarity between cell cofiring during SWRs and during theta are shown for each condition (see Materials and Methods). Each session produced one · on each plot. For all four conditions, the cell assemblies active during SWR were more similar to those seen during behavior (theta) than would be expected from either random control, including SWAP (preserving timing and ensemble firing properties) and SHUFF (preserving the overall firing rate of each neuron). Note that the overall condition is an analysis over all sessions, not an average of the other three conditions. On each plot, the horizontal line at negative mean log-likelihood = 3 marks p = 0.05. The marks above this line imply significant reactivation on that session. The numbers above each column indicate the number of sessions with significant reactivation. The numbers in bold indicate more sessions with significant reactivation than would be expected by chance. ALL, Overall, including all three tasks, p < 10⁻¹⁰; LT, p < 10⁻⁸; CF, p < 0.00001; CG, p < 0.0001.

Figure 6.

The assemblies became more coherent through the session. The sharp wave–ripple complexes in each session were divided into two halves by the median occurring sharp waves, providing the same number of SWRs in two blocks (an early block and a late block). If the cell assemblies cofiring in the SWRs become more similar to the cell assemblies occurring during theta, we would expect the similarity to increase between the two blocks. The similarity did increase for the linear track, and for the overall condition. But the increase was not significant for the two-dimensional conditions. One-sided nonparametric Wilcoxon's signed rank tests were used (Zar, 1999). ALL, Overall, including all three tasks, p < 0.01; LT, p < 0.05; CF, p < 0.25; CG, p < 0.10.

Figure 7.

Dependence of SWR emission on the sequential repetitiveness of the behavior. Number of SWR events normalized by time spent in nontheta states as a function of lap number and behavioral entropy. The color bar indicates SWR emission rate, measured as seconds⁻¹. SWR emission increased with lower entropy (more regular paths) and on later laps (with more experience). ALL, Each lap for each session on each task (LT, CF, or CG) contributed one three-dimensional point to the analysis. For each bin, points were radially averaged to determine average SW emission given the cumulative regularity and experience. Statistics: Stepwise regression on raw (i.e., unaveraged) data showed an effect of lap number, p < 0.00001; an effect of entropy, p < 0.00001; and an interaction between the two, p < 0.00001. LT, Same as ALL except only LT sessions were used. Statistics: Stepwise regression on raw data showed an effect of lap number, p < 0.00001; an effect of entropy, p < 0.0005; and an interaction between the two, p < 0.00001. CF, Same as ALL except only CF sessions were used. Statistics: Stepwise regression on raw data showed an effect of lap number, p < 0.002 (ns, by multiple comparisons); an effect of entropy, p < 0.001; and an interaction between the two, p < 0.00005. CG, Same as ALL except only CG sessions were used. Statistics: Stepwise regression on raw data showed an effect of lap number, p > 0.002 (ns, by multiple comparisons); an effect of entropy, p > 0.31 (ns); and an interaction between the two, p < 0.0005.

Figure 8.

Dependence of reactivation on the sequential repetitiveness of the behavior. Reactivation similarity (measured as negative log-likelihood of similarity relative to randomness) as a function of behavioral entropy and total time spent in theta. Statistics: Stepwise regression showed an effect of entropy, p < 0.005; for time in theta, p < 0.05; and an interaction between the two, p < 0.001.

Figure 9.

Dependence of reactivation on the order of task experience. Reactivation similarity (measured as negative log likelihood of similarity relative to randomness) as a function of order of the tasks. The line indicates the significance threshold, p = 0.05. Statistics: All four conditions show significant reactivation, p < 0.001, binomial test. Significant increase in reactivation of other tasks with order (p < 0.001, sign test) but no increase in reactivation of the task being run (p = 0.09, sign test).

Figure 10.

Change in SWR with experience on the multiple-T task. SWR emission in both CA3 and CA1 increased over the first 20 laps but then remained stable for the rest of the session. Error bars indicate SEM.