STRUCTURAL DYNAMICS OF SYNAPSES AND SYNAPTIC COMPONENTS

Citation
Jr. Wolff et al., STRUCTURAL DYNAMICS OF SYNAPSES AND SYNAPTIC COMPONENTS, Behavioural brain research, 66(1-2), 1995, pp. 13-20
Citations number
43
Categorie Soggetti
Neurosciences,Neurosciences
Journal title
ISSN journal
01664328
Volume
66
Issue
1-2
Year of publication
1995
Pages
13 - 20
Database
ISI
SICI code
0166-4328(1995)66:1-2<13:SDOSAS>2.0.ZU;2-I
Abstract
Learning and memory formation are apparently based on cascades of mole cular and cellular processes with increasing time constants (ms to day s and weeks), but even the most long-lasting effects are transient. Me mory traces may permanently modify the behavior (activity patterns, ge ne expression) of neurons and neuronal networks. Therefore the questio n is raised whether our current view on the stability of synapses unde r normal conditions is tenable. Evidence is reviewed suggesting that a s direct or indirect effects of modifications in bioelectrical activit y and chemical trophicity, synapses may be remodeled and removed withi n days and weeks, and possibly within hours. Accordingly, species-spec ific connectivity patterns are not restricted to the standard architec ture of the CNS, but (morpho-)genetics allow for a considerable number of alternative wiring patterns, which appear under unusual conditions during ontogenesis and in adulthood. Our present knowledge suggests t hat, rather than the formation of synapses, they are a selective proce ss. Until now there is no direct method of measuring either synaptic r eorganization or the average life span of synapses. Specific cases, ho wever, allow to estimate synapse turnover during ontogenesis, at its l owest possible level. Such data suggest that each synapse is on averag e remodeled or replaced several to many times during normal developmen tal, e.g. in the cerebral cortex of Marmoset monkeys at the very least 5 to 10 times (corresponding to 250 million synapses eliminated per h our in area 17!). It is discussed how the consequences of synapse turn over could be utilized by learning processes. Conclusions are followed by an outlook.