SUMMARY Long-term storage of episodic memories is hypothesized to result from the off-line transfer of information from the hippocampus to neocortex, allowing a hippocampal-independent cortical representation to emerge. However, off-line hippocampal-cortical interactions have not been demonstrated to be linked with long-term memory. Here, using functional magnetic resonance imaging, we examined if hippocampal-cortical BOLD correlations during rest following an associative encoding task are related to later associative memory performance. Our data show enhanced functional connectivity between the hippocampus and a portion of the lateral occipital complex (LO) during rest following a task with high subsequent memory compared to pre-task baseline resting connectivity. This effect is not seen during rest following a task with poor subsequent memory. Furthermore, the magnitude of hippocampal-LO correlations during post-task rest predicts individual differences in later associative memory. These results demonstrate, for the first time, the importance of post-experience resting brain correlations for memory for recent experiences.

Summary The hippocampus is proposed to switch between memory encoding and retrieval by continually computing the overlap between what is expected and what is encountered. Central to this hypothesis is that area CA1 performs this calculation. However, empirical evidence for this is lacking. To test the theoretical role of area CA1 in match/mismatch detection, we had subjects study complex stimuli and then, during high-resolution fMRI scanning, make memory judgments about probes that either matched or mismatched expectations. More than any other hippocampal subfield, area CA1 displayed responses consistent with a match/mismatch detector. Specifically, the responses in area CA1 tracked the total number of changes present in the probe. Additionally, area CA1 was sensitive to both behaviorally relevant and irrelevant changes, a key feature of an automatic comparator. These results are consistent with, and provide the first evidence in humans for, the theoretically important role of area CA1 as a match/mismatch detector.

This paper reviews the fate of the central ideas behind the complementary learning systems (CLS) framework as originally articulated in McClelland, McNaughton, and O'Reilly (1995). This framework explains why the brain requires two differentially specialized learning and memory systems, and it nicely specifies their central properties (i.e., the hippocampus as a sparse, pattern-separated system for rapidly learning episodic memories, and the neocortex as a distributed, overlapping system for gradually integrating across episodes to extract latent semantic structure). We review the application of the CLS framework to a range of important topics, including the following: the basic neural processes of hippocampal memory encoding and recall, conjunctive encoding, human recognition memory, consolidation of initial hippocampal learning in cortex, dynamic modulation of encoding versus recall, and the synergistic interactions between hippocampus and neocortex. Overall, the CLS framework remains a vital theoretical force in the field, with the empirical data over the past 15 years generally confirming its key principles.

The learning mechanism in the hippocampus has almost universally been assumed to be Hebbian in nature, where individual neurons in an engram join together with synaptic weight increases to support facilitated recall of memories later. However, it is also widely known that Hebbian learning mechanisms impose significant capacity constraints, and are generally less computationally powerful than learning mechanisms that take advantage of error signals. We show that the differential phase relationships of hippocampal subfields within the overall theta rhythm enable a powerful form of error-driven learning, which results in significantly greater capacity, as shown in computer simulations. In one phase of the theta cycle, the bidirectional connectivity between CA1 and entorhinal cortex can be trained in an error-driven fashion to learn to effectively encode the cortical inputs in a compact and sparse form over CA1. In a subsequent portion of the theta cycle, the system attempts to recall an existing memory, via the pathway from entorhinal cortex to CA3 and CA1. Finally the full theta cycle completes when a strong target encoding representation of the current input is imposed onto the CA1 via direct projections from entorhinal cortex. The difference between this target encoding and the attempted recall of the same representation on CA1 constitutes an error signal that can drive the learning of CA3 to CA1 synapses. This CA3 to CA1 pathway is critical for enabling full reinstatement of recalled hippocampal memories out in cortex. Taken together, these new learning dynamics enable a much more robust, high-capacity model of hippocampal learning than was available previously under the classical Hebbian model.

Benefits in long-term memory retention and generalization have been shown to be related to sleep-dependent processes, which correlate with neural oscillations as measured by changes in electric potential. The specificity and causal role of these oscillations, however, are still poorly understood. Here, we investigated the potential for augmenting endogenous slow-wave (SW) oscillations in humans with closed-loop transcranial alternating current stimulation (tACS) with an aim toward enhancing the consolidation of recent experiences into long-term memory. Sixteen (three female) participants were trained presleep on a target detection task identifying targets hidden in complex visual scenes. During post-training sleep, closed-loop SW detection and stimulation were used to deliver tACS matching the phase and frequency of the dominant oscillation in the range of 0.5-1.2 Hz. Changes in performance were assessed the following day using test images that were identical to the training ("repeated"), and images generated from training scenes but with novel viewpoints ("generalized"). Results showed that active SW tACS during sleep enhanced the postsleep versus presleep target detection accuracy for the generalized images compared with sham nights, while no significant change was found for repeated images. Using a frequency-agnostic clustering approach sensitive to stimulation-induced spectral power changes in scalp EEG, this behavioral enhancement significantly correlated with both a poststimulation increase and a subsequent decrease in measured spectral power within the SW band, which in turn showed increased coupling with spindle amplitude. These results suggest that augmenting endogenous SW oscillations can enhance consolidation by specifically improving generalization over recognition or cued recall. This human study demonstrates the use of a closed-loop noninvasive brain stimulation method to enhance endogenous neural oscillations during sleep with the effect of improving consolidation of recent experiences into long-term memory. Here we show that transient slow oscillatory transcranial alternating current stimulation (tACS) triggered by endogenous slow oscillations and matching their frequency and phase can increase slow-wave power and coupling with spindles. Further, this increase correlates with overnight improvements in generalization of recent training to facilitate performance in a target detection task. We also provide novel evidence for a tACS-induced refractory period following the tACS-induced increase. Here slow-wave power is temporarily reduced relative to sham stimulation, which nonetheless maintains a positive relationship with behavioral improvements.