B矩阵法的活性位点模型
Active Sites model for the B-Matrix Approach
摘要：
本文继续Kak博士提出的Hebbian学习中的B矩阵方法的工作。它报告的结果，提高记忆检索能力的赫布神经网络，实现了B-矩阵的方法。以前，从网络中检索记忆的方法是分别夹住所有单个神经元，并验证这些记忆的完整性。在这里，我们提出了一个网络，能够在训练阶段识别网络中的“活动位点”，并使用这些“活动位点”来生成从这些神经元中检索到的记忆。当多个神经元被箝位时，提出了三种从邻近矩阵获得网络更新顺序的方法。然后，我们提出了一个新的方法之间的比较，经典的情况下，也在方法本身。
英文摘要：
This paper continues on the work of the B-Matrix approach in hebbian learning proposed by Dr. Kak. It reports the results on methods of improving the memory retrieval capacity of the hebbian neural network which implements the B-Matrix approach. Previously, the approach to retrieving the memories from the network was to clamp all the individual neurons separately and verify the integrity of these memories. Here we present a network with the capability to identify the "active sites" in the network during the training phase and use these "active sites" to generate the memories retrieved from these neurons. Three methods are proposed for obtaining the update order of the network from the proximity matrix when multiple neurons are to be clamped. We then present a comparison between the new methods to the classical case and also among the methods themselves.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
