The Bionic Ear Institute, Melbourne VIC, Australia.
Front Comput Neurosci. 2010 Sep 10;4. doi: 10.3389/fncom.2010.00023. eCollection 2010.
Recent results about spike-timing-dependent plasticity (STDP) in recurrently connected neurons are reviewed, with a focus on the relationship between the weight dynamics and the emergence of network structure. In particular, the evolution of synaptic weights in the two cases of incoming connections for a single neuron and recurrent connections are compared and contrasted. A theoretical framework is used that is based upon Poisson neurons with a temporally inhomogeneous firing rate and the asymptotic distribution of weights generated by the learning dynamics. Different network configurations examined in recent studies are discussed and an overview of the current understanding of STDP in recurrently connected neuronal networks is presented.
最近关于在反复连接的神经元中尖峰时间依赖可塑性(STDP)的研究结果进行了回顾,重点关注权重动态与网络结构出现之间的关系。特别是,比较和对比了单个神经元的传入连接和递归连接的两种情况下突触权重的演变。所使用的理论框架基于具有时变发放率的泊松神经元和由学习动力学产生的权重渐近分布。讨论了最近研究中检查的不同网络配置,并介绍了对递归连接神经元网络中 STDP 的当前理解的概述。