Frankfurt Institute for Advanced Studies, Frankfurt am Main, 60594, Germany.
Neural Comput. 2011 Nov;23(11):2770-97. doi: 10.1162/NECO_a_00195. Epub 2011 Aug 18.
We present a model for the emergence of ordered fiber projections that may serve as a basis for invariant recognition. After invariance transformations are self-organized, so-called control units competitively activate fiber projections for different transformation parameters. The model builds on a well-known ontogenetic mechanism, activity-based development of retinotopy, and it employs activity blobs of varying position and size to install different transformations. We provide a detailed analysis for the case of 1D input and output fields for schematic input patterns that shows how the model is able to develop specific mappings. We discuss results that show that the proposed learning scheme is stable for complex, biologically more realistic input patterns. Finally, we show that the model generalizes to 2D neuronal fields driven by simulated retinal waves.
我们提出了一个有序纤维投射出现的模型，它可以作为不变性识别的基础。在不变性变换被自组织之后，所谓的控制单元会为不同的变换参数竞争地激活纤维投射。该模型基于一种著名的发生机制，即基于活动的视网膜拓扑发生，并且它使用具有不同位置和大小的活动团块来安装不同的变换。我们提供了一个详细的分析，针对 1D 输入和输出场的情况，用于示意性输入模式，展示了模型如何能够发展出特定的映射。我们讨论了结果，表明所提出的学习方案对于复杂的、更符合生物学的输入模式是稳定的。最后，我们表明该模型可以推广到由模拟视网膜波驱动的 2D 神经元场。
