We tested the hypothesis that rapid eye movement (REM) sleep is important for complex associative learning by restricting rats from entering REM sleep for 4 h either immediately after training on an eight-box spatial task (0-4 REMr) or 4 h following training (4)(5)(6)(7)(8). Both groups of REM-restricted rats eventually reached the same overall performance level as did nonrestricted controls, but 0-4 REMr animals were delayed in their improvement in the first few days and lagged behind controls in the middle portion of the training period. More importantly, performance gains of 0-4 REMr rats depended more on simple local cues throughout the 15-d study since, unlike control and 4-8 REMr animals, their error rate increased after daily disruption of the relationship between local (intramaze) cues and the food reward. Thus, although overall performance was only subtly and transiently impaired, due to the ability to use alternate, nonspatial behavioral strategies, complex associative (spatial) learning was persistently impaired by restricting REM for a short critical period each day. Memory systems and REM sleepWhile evidence suggests that people remember fewer details of previous experience after a night of little or no sleep (Dinges et al. 1997;Van Dongen et al. 2003), the nature of the dependence of learning and memory on sleep is largely unknown. Much of the difficulty in making a strong argument for the role of sleep in learning (Vertes and Eastman 2000;Siegel 2001) stems from the often temporary and subtle variations in performance exhibited after sleep deprivation. For example, people sleeping relatively few hours per night can still adequately perform most workrelated functions most of the time, especially with the use of a stimulant (Bonnet and Arand 1994; Westensten et al. 2002;Wyatt et al. 2004). Sleep deprivation increases the areas and number of brain regions activated in completing a complex or split-attention task (Drummond et al. 2000;Drummond et al. 2001). As in the case when an area of the brain normally involved in a task is compromised, other brain regions may be invoked to compensate. For example, when age or local anesthetic reduces hippocampal function, performance on a spatial task can remain adequate by the use of working memory rehearsal or nonhippocampus-dependent cue strategies (Barnes et al. 1980;Rapp et al. 1987;Poe et al. 2000a). Under sleep deprivation, the increased brain response may allow people to function normally until a crisis or critical multifactorial decision point arrives. At this point the reserve processing areas, already tapped for normal processing, cannot support the additional load and judgment can be disastrously impaired. Lack of sleep was implicated in four different nuclear power plant accidents and near accidents as well as in decisions surrounding disastrous and near disastrous space shuttle launches (Mitler et al. 1988). Thus, although overall performance may be only mildly impaired or not impaired at all under standard conditions, high-level processing mechanis...

Study Objectives: Investigators assign sleep-waking states using brain activity collected from a single site, with the assumption that states occur at the same time throughout the brain. We sought to determine if sleep-waking states differ between two separate structures: the hippocampus and neocortex. Methods: We measured electrical signals (electroencephalograms and electromyograms) during sleep from the hippocampus and neocortex of five freely behaving adult male rats. We assigned sleep-waking states in 10-sec epochs based on standard scoring criteria across a 4-h recording, then analyzed and compared states and signals from simultaneous epochs between sites. Results: We found that the total amount of each state, assigned independently using the hippocampal and neocortical signals, was similar between the hippocampus and neocortex. However, states at simultaneous epochs were different as often as they were the same (P = 0.82). Furthermore, we found that the progression of states often flowed through asynchronous state-pairs led by the hippocampus. For example, the hippocampus progressed from transitionto-rapid eye movement sleep to rapid eye movement sleep before the neocortex more often than in synchrony with the neocortex (38.7 ± 16.2% versus 15.8 ± 5.6% mean ± standard error of the mean). Conclusions: We demonstrate that hippocampal and neocortical sleep-waking states often differ in the same epoch. Consequently, electrode location affects estimates of sleep architecture, state transition timing, and perhaps even percentage of time in sleep states. Therefore, under normal conditions, models assuming brain state homogeneity should not be applied to the sleeping or waking brain.

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We propose a novel measure to detect temporal ordering in the activity of individual neurons in a local network, which is thought to be a hallmark of activity-dependent synaptic modifications during learning. The measure, called Causal Entropy, is based on the time-adaptive detection of asymmetries in the relative temporal patterning between neuronal pairs. We characterize properties of the measure on both simulated data and experimental multiunit recordings of hippocampal neurons from the awake, behaving rat, and show that the metric can more readily detect those asymmetries than standard cross correlation-based techniques, especially since the temporal sensitivity of causal entropy can detect such changes rapidly and dynamically.