During slow wave sleep (SWS), traces of neuronal activity patterns from preceding behavior can be observed in rat hippocampus and neocortex. The spontaneous reactivation of these patterns is manifested as the reinstatement of the distribution of pairwise firing-rate correlations within a population of simultaneously recorded neurons. The effects of behavioral state [quiet wakefulness, SWS, and rapid eye movement (REM)], interactions between two successive spatial experiences, and global modulation during 200 Hz electroencephalographic (EEG) "ripples" on pattern reinstatement were studied in CA1 pyramidal cell population recordings. Pairwise firing-rate correlations during often repeated experiences accounted for a significant proportion of the variance in these interactions in subsequent SWS or quiet wakefulness and, to a lesser degree, during SWS before the experience on a given day. The latter effect was absent for novel experiences, suggesting that a persistent memory trace develops with experience. Pattern reinstatement was strongest during sharp wave-ripple oscillations, suggesting that these events may reflect system convergence onto attractor states corresponding to previous experiences. When two different experiences occurred in succession, the statistically independent effects of both were evident in subsequent SWS. Thus, the patterns of neural activity reemerge spontaneously, and in an interleaved manner, and do not necessarily reflect persistence of an active memory (i.e., reverberation). Firing-rate correlations during REM sleep were not related to the preceding familiar experience, possibly as a consequence of trace decay during the intervening SWS. REM episodes also did not detectably influence the correlation structure in subsequent SWS, suggesting a lack of strengthening of memory traces during REM sleep, at least in the case of familiar experiences.

We thank J. Bliss, L. Church, C. Duffield, and M. Galganski for help with animal training and recording; R. D'Monte for help with histology; and

Two types of neurons in the rat brain have been proposed to participate in spatial learning and navigation: place cells, which fire selectively in specific locations of an environment and which may constitute key elements of cognitive maps, and head direction cells, which fire selectively when the rat's head is pointed in a specific direction and which may serve as an internal compass to orient the cognitive map. The spatially and directionally selective properties of these cells arise from a complex interaction between input from external landmarks and from idiothetic cues; however, the exact nature of this interaction is poorly understood. To address this issue, directional information from visual landmarks was placed in direct conflict with directional information from idiothetic cues. When the mismatch between the two sources of information was small (45 degrees), the visual landmarks had robust control over the firing properties of place cells; when the mismatch was larger, however, the firing fields of the place cells were altered radically, and the hippocampus formed a new representation of the environment. Similarly, the visual cues had control over the firing properties of head direction cells when the mismatch was small (45 degrees), but the idiothetic input usually predominated over the visual landmarks when the mismatch was larger. Under some conditions, when the visual landmarks predominated after a large mismatch, there was always a delay before the visual cues exerted their control over head direction cells. These results support recent models proposing that prewired intrinsic connections enable idiothetic cues to serve as the primary drive on place cells and head direction cells, whereas modifiable extrinsic connections mediate a learned, secondary influence of visual landmarks.

Hippocampal 'place' cells and the head-direction cells of the dorsal presubiculum and related neocortical and thalamic areas appear to be part of a preconfigured network that generates an abstract internal representation of two-dimensional space whose metric is self-motion. It appears that viewpoint-specific visual information (e.g. landmarks) becomes secondarily bound to this structure by associative learning. These associations between landmarks and the preconfigured path integrator serve to set the origin for path integration and to correct for cumulative error. In the absence of familiar landmarks, or in darkness without a prior spatial reference, the system appears to adopt an initial reference for path integration independently of external cues. A hypothesis of how the path integration system may operate at the neuronal level is proposed.

No abstract