Encoding and retention of information in memory are associated with a sustained increase in the amplitude of neuronal oscillations for up to several seconds. We reasoned that coordination of oscillatory activity over time might be important for memory and, therefore, that the amplitude modulation of oscillations may be abnormal in Alzheimer disease (AD). To test this hypothesis, we measured magnetoencephalography (MEG) during eyes-closed rest in 19 patients diagnosed with early-stage AD and 16 agematched control subjects and characterized the autocorrelation structure of ongoing oscillations using detrended fluctuation analysis and an analysis of the life-and waiting-time statistics of oscillation bursts. We found that Alzheimer's patients had a strongly reduced incidence of alpha-band oscillation bursts with long life-or waiting-times (< 1 s) over temporo-parietal regions and markedly weaker autocorrelations on long time scales (1-25 seconds). Interestingly, the life-and waiting-times of theta oscillations over medial prefrontal regions were greatly increased. Whereas both temporo-parietal alpha and medial prefrontal theta oscillations are associated with retrieval and retention of information, metabolic and structural deficits in early-stage AD are observed primarily in temporo-parietal areas, suggesting that the enhanced oscillations in medial prefrontal cortex reflect a compensatory mechanism. Together, our results suggest that amplitude modulation of neuronal oscillations is important for cognition and that indices of amplitude dynamics of oscillations may prove useful as neuroimaging biomarkers of early-stage AD.Alzheimer's disease ͉ magnetoencephalography ͉ neuronal oscillations ͉ resting-state brain activity ͉ temporal correlations P sychological and neuroimaging data suggest that the brain performs many important functions during rest, such as retrieval and manipulation of information in short-term memory, and problem-solving and planning (1, 2). These resting-state functions may represent an essential aspect of human selfawareness and are susceptible to impairment in brain-related disorders including depression, schizophrenia, and dementia (3).Neuroimaging has identified anatomical patterns of activity that are remarkably consistent across resting-state experiments, most notably in the precuneus, lateral parietal and medial prefrontal cortices (4, 5). The existence of such a ''resting-state network'' has been suggested to reflect a ''default mode'' of brain operation in the absence of goal-directed behavior (6). Coordination of anatomically distributed activity during rest has been studied extensively by computing correlations between neuronal signals from different brain areas (Fig. 1). This approach has revealed aberrant resting-state networks in Alzheimer disease (AD) (7-9) and other disorders (4, 10, 11).For cognitive processing, coordination of local brain activity over time may be just as important as the coordination of simultaneous activity in anatomically distinct brain regions and may be refl...

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How does the brain learn those visual features that are relevant for behavior? In this article, we focus on two factors that guide plasticity of visual representations. First, reinforcers cause the global release of diffusive neuromodulatory signals that gate plasticity. Second, attentional feedback signals highlight the chain of neurons between sensory and motor cortex responsible for the selected action. We here propose that the attentional feedback signals guide learning by suppressing plasticity of irrelevant features while permitting the learning of relevant ones. By hypothesizing that sensory signals that are too weak to be perceived can escape from this inhibitory feedback, we bring attentional learning theories and theories that emphasized the importance of neuromodulatory signals into a single, unified framework. Perception improves with trainingVisual perception improves with practice. A birdwatcher sees differences between birds that are invisible to the untrained eye. To gain an understanding of perceptual learning one can compare the perception of bird experts to the perception of subjects with other interests [as explained in detail in 1]. Alternatively, one can study perceptual learning in the laboratory, which has produced many important insights. Training improves perception, even in adult observers, provided they are willing to invest some effort in the task. Subjects typically have to train for a few hundred trials per day over a few days before perceptual improvements are noticeable. Under these conditions, subjects can even become better in discriminating between basic features, for example, between subtle variations in the orientation or motion direction of a visual stimulus [2]. Once perceptual learning has occurred, it is persistent and can last for many months [3] or years [4]. The learning effects are often specific so that perceptual improvements in a particular version of a task do not generalize to other versions. Performance improvements are not observed, for example, if the test stimulus has a different orientation [4 -6], motion direction [7 , 8] or contrast [9 , 10] than the trained stimulus. Moreover, the training effects are often retinotopically specific. After training in one region of the visual field, the improvement in performance does not transfer to other visual field locations [4 , 6 -9 , 11 , 12], although special learning procedures can cause better generalization [13 , 14].© 2009 Elsevier Ltd. All rights reserved. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. What are the mechanisms that determine perceptual learni...

We present a simulation framework, called NETMORPH, for the developmental generation of 3D large-scale neuronal networks with realistic neuron morphologies. In NETMORPH, neuronal morphogenesis is simulated from the perspective of the individual growth cone. For each growth cone in a growing axonal or dendritic tree, its actions of elongation, branching and turning are described in a stochastic, phenomenological manner. In this way, neurons with realistic axonal and dendritic morphologies, including neurite curvature, can be generated. Synapses are formed as neurons grow out and axonal and dendritic branches come in close proximity of each other. NETMORPH is a flexible tool that can be applied to a wide variety of research questions regarding morphology and connectivity. Research applications include studying the complex relationship between neuronal morphology and global patterns of synaptic connectivity. Possible future developments of NETMORPH are discussed.

Lasting alterations in sensory input trigger massive structural and functional adaptations in cortical networks. The principles governing these experience-dependent changes are, however, poorly understood. Here, we examine whether a simple rule based on the neurons' need for homeostasis in electrical activity may serve as driving force for cortical reorganization. According to this rule, a neuron creates new spines and boutons when its level of electrical activity is below a homeostatic set-point and decreases the number of spines and boutons when its activity exceeds this set-point. In addition, neurons need a minimum level of activity to form spines and boutons. Spine and bouton formation depends solely on the neuron's own activity level, and synapses are formed by merging spines and boutons independently of activity. Using a novel computational model, we show that this simple growth rule produces neuron and network changes as observed in the visual cortex after focal retinal lesions. In the model, as in the cortex, the turnover of dendritic spines was increased strongest in the center of the lesion projection zone, while axonal boutons displayed a marked overshoot followed by pruning. Moreover, the decrease in external input was compensated for by the formation of new horizontal connections, which caused a retinotopic remapping. Homeostatic regulation may provide a unifying framework for understanding cortical reorganization, including network repair in degenerative diseases or following focal stroke.