Suppr超能文献

通过依赖于脉冲时间的突触可塑性实现竞争性赫布学习。

Competitive Hebbian learning through spike-timing-dependent synaptic plasticity.

作者信息

Song S, Miller K D, Abbott L F

机构信息

Volen Center for Complex Systems and Department of Biology, Brandeis University, Waltham, Massachusetts 02254-9110, USA.

出版信息

Nat Neurosci. 2000 Sep;3(9):919-26. doi: 10.1038/78829.

Abstract

Hebbian models of development and learning require both activity-dependent synaptic plasticity and a mechanism that induces competition between different synapses. One form of experimentally observed long-term synaptic plasticity, which we call spike-timing-dependent plasticity (STDP), depends on the relative timing of pre- and postsynaptic action potentials. In modeling studies, we find that this form of synaptic modification can automatically balance synaptic strengths to make postsynaptic firing irregular but more sensitive to presynaptic spike timing. It has been argued that neurons in vivo operate in such a balanced regime. Synapses modifiable by STDP compete for control of the timing of postsynaptic action potentials. Inputs that fire the postsynaptic neuron with short latency or that act in correlated groups are able to compete most successfully and develop strong synapses, while synapses of longer-latency or less-effective inputs are weakened.

摘要

发育和学习的赫布模型既需要依赖活动的突触可塑性,也需要一种能在不同突触间引发竞争的机制。实验观察到的一种长期突触可塑性形式,我们称之为尖峰时间依赖可塑性(STDP),它取决于突触前和突触后动作电位的相对时间。在建模研究中,我们发现这种突触修饰形式能够自动平衡突触强度,使突触后发放变得不规则,但对突触前尖峰时间更敏感。有人认为,体内的神经元在这样一种平衡状态下运作。可被STDP修饰的突触会竞争对突触后动作电位时间的控制。那些以短潜伏期激发突触后神经元或成组协同作用的输入能够最成功地竞争并形成强突触,而潜伏期较长或效果较差的输入的突触则会被削弱。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验