Learning-induced plasticity regulates hippocampal sharp wave-ripple drive

Abstract

Hippocampal sharp wave-ripples (SPW-Rs) and associated place-cell reactivations are crucial for spatial memory consolidation during sleep and rest. However, it remains unclear how learning and consolidation requirements influence and regulate subsequent SPW-R activity. Indeed, SPW-R activity has been observed not only following complex behavioral tasks, but also after random foraging in familiar environments, despite markedly different learning requirements. Because transient increases in SPW-R rates have been reported following training on memory tasks, we hypothesized that SPW-R activity following learning (but not routine behavior) could involve specific regulatory processes related to ongoing consolidation. Interfering with ripples would then result in a dynamic compensatory response only when initial memory traces required consolidation. Here we trained rats on a spatial memory task, and showed that subsequent sleep periods where ripple activity was perturbed by timed electrical stimulation were indeed characterized by increased SPW-R occurrence rates compared with control sleep periods where stimulations were slightly delayed in time and did not interfere with ripples. Importantly, this did not occur following random foraging in a familiar environment. We next showed that this dynamic response was abolished following injection of an NMDA receptor blocker (MK-801) before, but not after training. Our results indicate that NMDA receptor-dependent processes occurring during learning, such as network "tagging" and plastic changes, regulate subsequent ripple-mediated consolidation of spatial memory during sleep.

Keywords: consolidation; hippocampus; learning; memory; regulation; ripples.

Figure 1.

Experimental schedule. A–C, Behavioral conditions and stimulation protocols were alternated in a pseudorandom manner. For each behavioral condition (A, training on the maze; B, home cage; C, exploration of a familiar arena), stimulation protocols were paired for comparison (red crossed ripple icon, detection—stimulation; blue intact ripple icon, detection–delay–stimulation). D, Experimental schedule for all rats. Because conditions and protocols were intermingled in the same rat, training on the maze was limited and discontinuous, preventing assessment of progressive changes in task performance. (H, home cage; E, exploration of the familiar arena; L, learning the radial maze task; closed circle, immediate stimulation; open circle, delayed stimulation).

Figure 2.

Stimulation and counting protocols. A, In the test simulation protocol, automatic detection of early ripple cycles (black arrowhead) triggered an immediate single-pulse stimulation of the ventral hippocampal commissure (red arrowhead) that interrupted further ripple development. B, In the control simulation protocol, ripple detection (black arrowhead) triggered a delayed stimulation (blue arrowhead) that left the ripple intact. C, The number of stimulations, corresponding to interrupted (test) or intact (control, not shown) ripples, were counted during sleep and rest and their cumulative number plotted as a function of time. Linear regressions were then used to compare slopes between test and control conditions.

Figure 3.

Interfering with memory consolidation by selective ripple suppression triggers dynamic increases in SPW-R incidence after learning. A, During sleep and rest following training on the radial maze task (baited arms, red spots), single-pulse stimulation of the ventral hippocampal commissure was triggered either immediately upon ripple detection, preventing further ripple development, or following a random delay (80–120 ms), leaving ripples intact (Fig. 2). B, Example session pair. Ripples were counted as a function of time, during periods where they were either interrupted (orange curves) or left intact (blue curves). Ripple occurrence rates were computed as the slopes of the best-fit lines (y = ax dashed lines). C, SPW-R occurrence rates increased significantly in response to ripple suppression (paired t test, p = 0.0068, n = 12). Each dot corresponds to one session pair. The color indicates the p value for the comparison of the corresponding linear regression slopes, as illustrated in B (t tests: red and dark blue, p < 0.01; orange and light blue, p < 0.05; gray, p > 0.05).

Figure 4.

Stimulation during SPW-Rs does not induce detectable changes in network excitability or plasticity after learning. Slopes of field postsynaptic potentials (fPSPs) were measured during sleep and rest sessions following training on the radial maze. fPSP traces for one example session pair are shown in the top row (solid curve, mean; shaded area, SD). A, The fPSP slopes are not significantly different between the beginning and the end of the recording sessions (paired t test, p = 0.95, t = −0.069, n = 10, df = 9). B, The fPSP slopes are not significantly different between immediate and delayed stimulation (paired t test, p = 0.16, t = −1.531, n = 10, df = 9).

Figure 5.

A, In the control condition, rats foraged for food in a familiar arena before sleep and rest sessions. B, Ripple counts after an example foraging session pair. C, Following random foraging in the familiar arena, ripple occurrence rates were not significantly different (paired t test, p = 0.5405, n = 11) whether ripples were suppressed (y-axis) or left intact (x-axis).

Figure 6.

NMDA receptor blockade during training abolishes SPW-R upregulation. A–F, MK-801 was injected systemically either before (A–C) or after (D–F) training on the maze (complemented with control saline injections). During subsequent sleep and rest, ripples were either suppressed or left intact. Each injection was followed by a 15 min delay before training or sleep/rest recordings (white rectangles). B, E, Ripple counts for an example session pair when NMDA receptors were blocked either during (B) or after (E) training. C, F, SPW-R upregulation is abolished if MK-801 is administered before (C; paired t test, p = 0.0849, n = 12) but not after (F; paired t test, p = 0.0059, n = 11) training.