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这种学习规则的基本思想就是：通过不断地优化变异随机选择的连接权矩阵元，从而使网络在给定的训练目标下达到整体最优。
The basic idea of this learning rule is to obtain a certain optimization by continuously changing the elements of coupling matrix selected randomly.
交叉视觉皮层模型(ICM)在处理图像时，会因连接矩阵权值设置不当而产生错误。
When Intersecting Cortical Model (ICM) processing the images, it generates errors for improper operation of connection matrix.
当网络连接权值矩阵的最小特征值大于激活函数导数的倒数时，网络并行收敛。
When the minimal eigenvalue of connection weights matrix is greater than the reciprocal of derivation of its neuron activation function, the network will be convergent in parallel update mode.
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