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Neurobiology and Complex Biosystem Modeling

  • George N. ReekeJr.Email author
Chapter
Part of the Topics in Biomedical Engineering International Book Series book series (ITBE)

Abstract

This chapter gives a brief summary of techniques for modeling neural tissue as a complex biosystem at the cellular, synaptic, and network levels. A sampling of the most often studied neuronal models with some of their salient characteristics is presented, ranging from the abstract rate-coded cell through the integrate-and-fire point neuron to the multicompartment neuron with a full range of ionic conductances. An indication is given of how the choice of a particular model will be determined by the interplay of prior knowledge about the system in question, the hypotheses being tested, and purely practical computational constraints. While interest centers on the more mature art of modeling functional aspects of neuronal systems as anatomically static, but functionally plastic adult structures, in a concluding section we look to near-future developments that may in principle allow network models to reflect the influence of mechanical, metabolic, and extrasynaptic signaling properties of both neurons and glia as the nervous system develops, matures, and perhaps suffers from disease processes. These comments will serve as an introduction to techniques for modeling tumor growth and other abnormal aspects of nervous system function that are covered in later chapters of this book (Part III, §6). Through the use of complex-systems modeling techniques, bringing together information that often in the past has been studied in isolation within particular subdisciplines of neuro- and developmental biology, one can hope to gain new insight into the interplay of genetic programs and the multitude of environmental factors that together control neural systems development and function.

Keywords

Purkinje Cell Cellular Automaton Neuronal Modeling Cerebellar Purkinje Cell Modeling Tumor Growth 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Inc. 2006

Authors and Affiliations

  1. 1.Laboratory of Biological ModelingThe Rockefeller UniversityNew York

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