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生物医学工程评论综述™

每年出版 6 

ISSN 打印: 0278-940X

ISSN 在线: 1943-619X

SJR: 0.262 SNIP: 0.372 CiteScore™:: 2.2 H-Index: 56

Indexed in

Plasticity, Learning, and Complexity in Spiking Networks

卷 40, 册 6, 2012, pp. 501-518
DOI: 10.1615/CritRevBiomedEng.2013006724
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摘要

Complexity is widespread in neuronal spike trains and propagation of spike activity, in that variations in measurements of neural activity are irregular, heterogeneous, non-stationary, transient, and scale-free. There are numerous possible reasons for this complexity, and numerous possible consequences for neural and behavioral function. The present review is focused on relationships among neural plasticity, learning, and complex spike dynamics in animal nervous systems, including those of humans. The literature on complex spike dynamics and mechanisms of synaptic plasticity are reviewed for the purpose of considering the roles that each might play for the other. That is, the roles of complex spike dynamics in learning and regulatory functions are considered, as well as the roles of learning and regulatory functions in generating complex spike dynamics. Experimental and computational studies from a range of disciplines and perspectives are discussed, and it is concluded that cognitive science and neuroscience have much to gain from investigating the adaptive aspects of complex spike dynamics for neural and cognitive function.

对本文的引用
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  4. Scharnhorst Kelsey S., Carbajal Juan P., Aguilera Renato C., Sandouk Eric J., Aono Masakazu, Stieg Adam Z., Gimzewski James K., Atomic switch networks as complex adaptive systems, Japanese Journal of Applied Physics, 57, 3S2, 2018. Crossref

  5. Singh Anuj, Tiwari Arvind Kumar, A Survey on Computational Models for Learning and Memory in Artificial Intelligence, SSRN Electronic Journal , 2019. Crossref

  6. Singh Anuj, Tiwari Arvind Kumar, A Survey on Computational Intelligence Techniques in Learning and Memory, in Computational Intelligence in Communications and Business Analytics, 1579, 2022. Crossref

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