Doctoral Training Centre for Neuroinformatics and Computational Neuroscience, University of Edinburgh, Edinburgh, United Kingdom.
Neural Netw. 2010 May;23(4):517-27. doi: 10.1016/j.neunet.2010.01.005. Epub 2010 Feb 10.
A model of topographic map refinement is presented which combines both weight plasticity and the formation and elimination of synapses, as well as both activity-dependent and activity-independent processes. The question of whether an activity-dependent process can refine a mapping created by an activity-independent process is addressed statistically. A new method of evaluating the quality of topographic projections is presented which allows independent consideration of the development of the centres and spatial variances of receptive fields for a projection. Synapse formation and elimination embed in the network topology changes in the weight distributions of synapses due to the activity-dependent learning rule used (spike-timing-dependent plasticity). In this model, the spatial variance of receptive fields can be reduced by an activity-dependent mechanism with or without spatially correlated inputs, but the accuracy of receptive field centres will not necessarily improve when synapses are formed based on distributions with on-average perfect topography.
提出了一种地形图细化模型，该模型结合了权重可塑性以及突触的形成和消除，以及依赖于活动和不依赖于活动的过程。从统计学角度探讨了依赖于活动的过程是否可以细化由不依赖于活动的过程创建的映射的问题。提出了一种评估地形投影质量的新方法，该方法允许独立考虑投影的中心的发展和感受野的空间方差。突触的形成和消除使得由于所使用的依赖于活动的学习规则（尖峰时间依赖性可塑性），突触的权重分布发生网络拓扑变化。在这个模型中，感受野的空间方差可以通过依赖于活动的机制来减小，无论是否具有空间相关的输入，但是当基于具有平均完美地形的分布形成突触时，感受野中心的准确性不一定会提高。
