Biomedical Engineering Reference
In-Depth Information
5.1
NEUROBIOLOGY AND COMPLEX
BIOSYSTEM MODELING
George N. Reeke Jr.
Laboratory of Biological Modeling,
The Rockefeller University, New York
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 multi-
compartment 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 knowl-
edge about the system in question, the hypotheses being tested, and purely practical com-
putational 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 ex-
trasynaptic 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 in-
troduction to techniques for modeling tumor growth and other abnormal aspects of nerv-
ous system function that are covered in later chapters of this topic (Part III, ยง6). Through
the use of complex-systems modeling techniques, bringing together information that of-
ten 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.
Address correspondence to: George N. Reeke Jr., Laboratory of Biological Modeling, The Rockefel-
ler University, 1230 York Avenue, New York, NY 10021 (reeke@rockefeller.edu)
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