Jankovic Marko, Ogawa Hidemitsu
Control Department, The Institute of Electrical Engineering Nikola Tesla, Koste Glavinica 8a, 11000 Belgrade, Serbia and Montenegro.
Int J Neural Syst. 2003 Aug;13(4):215-23. doi: 10.1142/S0129065703001595.
This paper presents one possible implementation of a transformation that performs linear mapping to a lower-dimensional subspace. Principal component subspace will be the one that will be analyzed. Idea implemented in this paper represents generalization of the recently proposed infinity OH neural method for principal component extraction. The calculations in the newly proposed method are performed locally--a feature which is usually considered as desirable from the biological point of view. Comparing to some other wellknown methods, proposed synaptic efficacy learning rule requires less information about the value of the other efficacies to make single efficacy modification. Synaptic efficacies are modified by implementation of Modulated Hebb-type (MH) learning rule. Slightly modified MH algorithm named Modulated Hebb Oja (MHO) algorithm, will be also introduced. Structural similarity of the proposed network with part of the retinal circuit will be presented, too.
本文提出了一种将线性映射到低维子空间的变换的可能实现方式。主成分子空间将是要分析的对象。本文实现的想法代表了最近提出的用于主成分提取的无穷OH神经方法的推广。新提出的方法中的计算是局部进行的——从生物学角度来看，这一特性通常被认为是可取的。与其他一些知名方法相比，所提出的突触效能学习规则在进行单一效能修改时，所需的关于其他效能值的信息更少。突触效能通过调制赫布型（MH）学习规则来修改。还将介绍一种略有修改的名为调制赫布奥贾（MHO）算法的MH算法。此外，还将展示所提出的网络与部分视网膜回路的结构相似性。
