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Biophysics of ComputationInformation Processing in Single Neurons$
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Christof Koch

Print publication date: 1998

Print ISBN-13: 9780195104912

Published to Oxford Scholarship Online: November 2020

DOI: 10.1093/oso/9780195104912.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 27 November 2021

Synaptic Plasticity

Synaptic Plasticity

Chapter:
(p.308) 13 Synaptic Plasticity
Source:
Biophysics of Computation
Author(s):

Christof Koch

Publisher:
Oxford University Press
DOI:10.1093/oso/9780195139853.003.0019

Animals live in an ever-changing environment to which they must continuously adapt. Adaptation in the nervous system occurs at every level, from ion channels and synapses to single neurons and whole networks. It operates in many different forms and on many time scales. Retinal adaptation, for example, permits us to adjust within minutes to changes of over eight orders of magnitude of brightness, from the dark of a moonless night to high noon. High-level memory—the storage and recognition of a person's face, for example—can also be seen as a specialized form of adaptation (see Squire, 1987). The ubiquity of adaptation in the nervous system is a radical but often underappreciated difference between brains and computers. With few exceptions, all modern computers are patterned according to the architecture laid out by von Neumann (1956). Here the adaptive elements—the random access memory (RAM)—are both physically and conceptually distinct from the processing elements, the central processing unit (CPU). Even proposals to incorporate massive amounts of so-called intelligent RAM (IRAM) directly onto any future processor chip fall well short of the degree of intermixing present in nervous systems (Kozyrakis et al., 1997). It is only within the last few years that a few pioneers have begun to demonstrate the advantages of incorporating adaptive elements at all stages of the computation into electronic circuits (Mead, 1990; Koch and Mathur, 1996; Diorio et al.,1996). For over a century (Tanzi, 1893; Ramón y Cajal, 1909, 1991), the leading hypothesis among both theoreticians and experimentalists has been that synoptic plasticity underlies most long-term behavioral plasticity. It has nevertheless been extremely difficult to establish a direct link between behavioral plasticity and its biophysical substrate, in part because most biophysical research is conducted with in vitro preparations in which a slice of the brain is removed from the organism, while behavior is best studied in the intact animal. In mammalian systems the problem is particularly acute, but combined pharmacological, behavioral, and genetic approaches are yielding promising if as yet incomplete results (Saucier and Cain, 1995; Cain, 1997; Davis, Butcher, and Morris, 1992; Tonegawa, 1995; McHugh et al., 1996; Rogan, Stäubli, LeDoux, 1997).

Keywords:   Associative conditioning, Behavioral plasticity, Carbon monoxide, Depression, Expression, Facilitation, Hair cells, Ionic currents, Learning

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