Reactivation of hippocampal cell assemblies: effects of behavioral state, experience, and EEG dynamics.

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.

Reactivation of neuronal ensembles in hippocampal dentate gyrus during sleep after spatial experience.

Patterns of neuronal activity recorded in CA1 of the hippocampus and in neocortex during waking-behavior, are reactivated during subsequent slow-wave sleep (SWS). It has been suggested that this reactivation may originate in the hippocampal CA3 region, where modifiable excitatory recurrent connections are abundant and where sharp-waves in which the reactivation is most robust, appear to arise. The present experiment investigated whether ensemble firing patterns of granule cells in the fascia dentata (FD), an area 'upstream' from CA3, are also reactivated during sleep. Populations of FD granule cells were recorded from during spatial behavior and during prior and subsequent SWS. firing rate correlations between cell-pairs with overlapping place fields were significantly enhanced during post behavioral sleep compared to pre behavioral sleep. Correlations between cells with non-overlapping place fields or which were silent during maze behavior, were not changed. Thus reactivation of experience-specific correlation states also occurs in granule cells during sleep. Because these cells do not have excitatory interconnections, but form a major input to CA3 pyramidal cells, current models predicted that sleep reactivation would appear first in CA3. There are, however, both extensive polysynaptic excitatory interactions among granule cells and feedback from CA3 pyramidal cells. Granule cells also receive indirect input from neocortical regions known to undergo trace reactivation. Although a simple model for a CA3 origin of the reactivation phenomenon cannot be confirmed, the present results extend our understanding of the generality of this phenomenon.

Interactions between idiothetic cues and external landmarks in the control of place cells and head direction cells.

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.

Neuronal mechanisms underlying the interaction between visual landmarks and path integration in the rat.

Place- and direction-specific firing properties of hippocampal and thalamic neurons are not strongly tied to visual landmarks when a rat is disoriented. These results suggest that rats rely more on path integration mechanisms than on landmarks, until they have learned that the landmarks are stable directional references.

Place cells, head direction cells, and the learning of landmark stability.

Previous studies have shown that hippocampal place fields are controlled by the salient sensory cues in the environment, in that rotation of the cues causes an equal rotation of the place fields. We trained rats to forage for food pellets in a gray cylinder with a single salient directional cue, a white card covering 90 degrees of the cylinder wall. Half of the rats were disoriented before being placed in the cylinder, in order to disrupt their internal sense of direction. The other half were not disoriented before being placed in the cylinder; for these rats, there was presumably a consistent relationship between the cue card and their internal direction sense. We subsequently recorded hippocampal place cells and thalamic head direction cells from both groups of rats as they moved in the cylinder; between some sessions the cylinder and cue card were rotated to a new direction. All rats were disoriented before recording. Under these conditions, the cue card had much weaker control over the place fields and head direction cells in the rats that had been disoriented during training than in the rats that had not been disoriented. For the former group, the place fields often rotated relative to the cue card or completely changed their firing properties between sessions. In all recording sessions, the head direction cells and place cells were strongly coupled. It appears that the strength of cue control over place cells and head direction cells depends on the rat's learned perception of the stability of the cues.

A model of the neural basis of the rat's sense of direction.

In the last decade the outlines of the neural structures subserving the sense of direction have begun to emerge. Several investigations have shed light on the effects of vestibular input and visual input on the head direction representation. In this paper, a model is formulated of the neural mechanisms underlying the head direction system. The model is built out of simple ingredients, depending on nothing more complicated than connectional specificity, attractor dynamics, Hebbian learning, and sigmoidal nonlinearities, but it behaves in a sophisticated way and is consistent with most of the observed properties of real head direction cells. In addition it makes a number of predictions that ought to be testable by reasonably straightforward experiments.