and phonological representations that capture better the relevant structure among the written and spoken forms of words. In a number of simulation experiments, networks using the new representations learn to read both regular and exception words, including low-frequency exception words, and yet are still able

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The authors present a unified account of 2 neural systems concerned with the development and expression of adaptive behaviors: a mesencephalic dopamine system for reinforcement learning and a “generic ” error-processing system associated with the anterior cingulate cortex. The existence

Thirty years of brain imaging research has converged to define the brain’s default network—a novel and only recently appreciated brain system that participates in internal modes of cog-nition. Here we synthesize past observations to provide strong evidence that the default net-work is a specific

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A. Norepinephrine signaling through �3-receptors leads to thermogenesis 280 B. Thermogenesis is due to activation of UCP1 through lipolysis 283 C. The �2-adrenergic pathway inhibits thermogenesis 288 D. The �1-adrenergic pathway and the cell membrane events 289 III. The Life of the Brown Adipocyte Is Under Adrenergic Control 290 A. In brown preadipocytes, norepinephrine promotes proliferation 292 B. In mature brown adipocytes, norepinephrine promotes differentiation 292 C. Norepinephrine directly regulates the expression of the UCP1 gene 293 D. Norepinephrine is an apoptosis inhibitor in brown adipocytes 294 IV. How Significant Is Brown Adipose Tissue? 295 A. Parameters of activation and recruitment 295 B. How to establish brown adipose tissue involvement 297 V. Thermoregulatory Thermogenesis 298 A. In acute cold, thermogenesis results from shivering 298 B. Classical nonshivering thermogenesis is entirely brown fat dependent 299

In the past, nearest neighbor algorithms for learning from examples have worked best in domains in which all features had numeric values. In such domains, the examples can be treated as points and distance metrics can use standard definitions. In symbolic domains, a more sophisticated treatment of the feature space is required. We introduce a nearest neighbor algorithm for learning in domains with symbolic features. Our algorithm calculates distance tables that allow it to produce real-valued distances between instances, and attaches weights to the instances to further modify the structure of feature space. We show that this technique produces excellent classification accuracy on three problems that have been studied by machine learning researchers: predicting protein secondary structure, identifying DNA promoter sequences, and pronouncing English text. Direct experimental comparisons with the other learning algorithms show that our nearest neighbor algorithm is comparable or superior ...

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