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2000 - Theoretical Neuroscience--Computational And Mathematical Modeling Of Neural Systems (The Mit Press).pdf

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2000 - Theoretical Neuroscience--Computational And Mathematical Modeling Of Neural Systems (The Mit Press).pdf

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2000 - Theoretical Neuroscience--Computational And Mathematical Modeling Of Neural Systems (The Mit Press).pdf

文档介绍

文档介绍:Preface
Theoretical analysis putational modeling are important tools for
characterizing what nervous systems do, determining how they function,
and understanding why they operate in particular ways. Neuroscience
passes approaches ranging from molecular and cellular studies to
human psychophysics and psychology. Theoretical neuroscience encour-
ages cross-talk among these sub-disciplines by pact rep-
resentations of what has been learned, building bridges between different
levels of description, and identifying unifying concepts and principles. In
this book, we present the basic methods used for these purposes and dis-
cuss examples in which theoretical approaches have yielded insight into
nervous system function.
The questions what, how, and why are addressed by descriptive, mecha-
nistic, and interpretive models, each of which we discuss in the following
chapters. Descriptive models summarize large amounts of experimental descriptive models
pactly yet accurately, thereby characterizing what neurons and
neural circuits do. These models may be based loosely on biophysical,
anatomical, and physiological findings, but their primary purpose is to de-
scribe phenomena not to explain them. Mechanistic models, on the other mechanistic models
hand, address the question of how nervous systems operate on the ba-
sis of known anatomy, physiology, and circuitry. Such models often form
a bridge between descriptive models couched at different levels. Inter-
pretive models putational and information-theoretic principles to interpretive models
explore the behavioral and cognitive significance of various aspects of ner-
vous system function, addressing the question of why nervous system op-
erate as they do.
It is often difficul to identify the appropriate level of modeling for a partic-
ular problem. A frequent mistake is to assume that a more detailed model
is necessarily superior. Because models act as bridges between levels of
understanding, they must b