Human sleep is a global state whose functions remain unclear. During much of sleep, cortical neurons undergo slow oscillations in membrane potential, which appear in electroencephalograms as slow wave activity (SWA) of <4 Hz. The amount of SWA is homeostatically regulated, increasing after wakefulness and returning to baseline during sleep. It has been suggested that SWA homeostasis may reflect synaptic changes underlying a cellular need for sleep. If this were so, inducing local synaptic changes should induce local SWA changes, and these should benefit neural function. Here we show that sleep homeostasis indeed has a local component, which can be triggered by a learning task involving specific brain regions. Furthermore, we show that the local increase in SWA after learning correlates with improved performance of the task after sleep. Thus, sleep homeostasis can be induced on a local level and can benefit performance.

Psychophysical studies of reaching movements suggest that hand kinematics are learned from errors in extent and direction in an extrinsic coordinate system, whereas dynamics are learned from proprioceptive errors in an intrinsic coordinate system. We examined consolidation and interference to determine if these two forms of learning were independent. Learning and consolidation of two novel transformations, a rotated spatial reference frame and altered intersegmental dynamics, did not interfere with each other and consolidated in parallel. Thus separate kinematic and dynamic models were constructed simultaneously based on errors computed in different coordinate frames, and possibly, in different sensory modalities, using separate working-memory systems. These results suggest that computational approaches to motor learning should include two separate performance errors rather than one.

The paradigm task A3task B3task A, which varies the time interval between task A and task B, has been used extensively to investigate the consolidation of motor memory. Consolidation is defined as resistance to retrograde interference (interference by task B on initial learning of task A). Consolidation has been demonstrated for simple skills, motor sequencing, and learning of force fields. In contrast, evidence to date suggests that visuomotor learning does not consolidate. We have shown previously that adaptation to a 30°screen-cursor rotation is faster and more complete on relearning 24 hr later. This improvement is prevented if a 30°counter-rotation is learned 5 min after the original rotation. Here, we sought to identify conditions under which rotation learning becomes resistant to interference by a counter-rotation. In experiment 1, we found that interference persists even when the counter-rotation is learned 24 hr after the initial rotation. In experiment 2, we removed potential anterograde interference (interference by task B on relearning of task A) by introducing washout blocks before all of the learning blocks. In contrast to experiment 1, we found resistance to interference (i.e., consolidation) when the counter-rotation was learned after 24 hr but not after 5 min. In experiment 3, we doubled the amount of initial rotation learning and found resistance to interference even after 5 min. Our results suggest that persistent interference is attributable to anterograde effects on memory retrieval. When anterograde effects are removed, rotation learning consolidates both over time and with increased initial training.

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. Differential cortical and subcortical activations in learning rotations and gains for reaching: a PET study. J Neurophysiol 91: 924 -933, 2004. First published October 1, 2003 10.1152/jn.00675.2003. Previous studies suggest that horizontal reaching movements are planned vectorially with independent specification of direction and extent. The transformation from visual to hand-centered coordinates requires the learning of a task-specific reference frame and scaling factor. We studied learning of a novel reference frame by imposing a screencursor rotation and learning of a scaling factor by imposing a novel gain. Previous work demonstrates that rotation and gain learning have different time courses and patterns of generalization. Here we used PET to identify and compare brain areas activated during rotation and gain learning, with a baseline motor-execution task as the subtracted control. Previous work has shown that the time courses of rotation and gain adaptation have a short rapid phase followed by a longer slow phase. We therefore also sought to compare activations associated with the rapid and slower phases of adaptation. We isolated the rapid phase by alternating opposite values of the rotation or gain every 16 movements. The rapid phase of rotation adaptation activated the preSMA. More complete adaptation to the rotation activated right ventral premotor cortex, right posterior parietal cortex, and the left lateral cerebellum. The rapid phase of gain learning only activated subcortical structures: bilateral putamen and left cerebellum. More complete gain learning failed to show any significant activation. We conclude that the time course of rotation adaptation is paralleled by a frontoparietal shift in activated cortical regions. In contrast, early gain adaptation involves only subcortical structures, which we suggest reflects a more automatic process of contextual recalibration of a scaling factor.