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The Retinal Müller Cell: Structure and Function (Perspectives in Vision Research)

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The human brain contains more than a billion neurons which interconnect to form networks that process, store, and recall sensory information. These neuronal activities are supported by a group of accessory brain cells coll- tively known as neuroglia. Surprisingly, glial cells are ten times more - merous than neurons, and occupy more than half the brain volume (Hydén, 1961). Although long considered a passive, albeit necessary, component of the nervous system, many interesting and unusual functional properties of glial cells are only now being brought to light. As a result, the status of these cellular elements is approaching parity with nerve cells as a subject for experimental study. The term glia (or glue) seems today to be a misnomer in view of the diverse functions attributed to glial cells. Experimental studies in the last three decades have clearly established that the behavior of glial cells is far from passive, and that they are at least as complex as neurons with regard to their membrane properties. In addition, glial cells are of importance in signal processing, cellular metabolism, nervous system development, and the pathophysiology of neurological diseases. The Müller cell of the ver- brate retina provides a splendid example of an accessory cell that exhibits features illustrating every aspect of the complex behavior now associated with glial cells.
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Matlab® for Engineers Explained
Matlab® for Engineers Explained
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Philosophy and Computer Science (Explorations in Philosophy)
Philosophy and Computer Science (Explorations in Philosophy)
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The Nature of Code: Simulating Natural Systems with Processing
The Nature of Code: Simulating Natural Systems with Processing
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Data Mining: Foundations and Practice (Studies in Computational Intelligence)
Data Mining: Foundations and Practice (Studies in Computational Intelligence)
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Probabilistic Databases (Synthesis Lectures on Data Management)
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Applied Econometrics with R (Use R!)
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Here is the first book on applied econometrics using the R system for statistical computing and graphics. It presents hands-on examples for a wide range of models, from classical linear regression models for cross-section to recent semiparametric extensions.

R is a language and environment for data analysis and
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