As PCNN is modulated by using the pulse-coupled synaptic mechanisms, it has a great potential for image processing in a complex real-world environment, especially in images. A new simplified pulse coupled neural network (SPCNN) is proposed. This new model uses the pixel intensity with the actual physical meanings as the input parameters instead of the abstract network parameters in the original SPCNN. In order to achieve this goal, we try to derive the general formulae of dynamic threshold and internal activity of the SPCNN according to the dynamic properties of neurons and then deduce the relationship between the pixel intensity and the abstract parameters. Then, the relationship is transformed into an objective optimization problem to obtain the appropriate abstract parameters. Finally, extensive experiments are conducted on seven widely used datasets to demonstrate the effectiveness of the proposed method and shown improvement on the salient region detection.

中文翻译：

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用于显着区域检测的改进的简化 PCNN 模型

由于 PCNN 是通过使用脉冲耦合突触机制进行调制的，因此它在复杂的现实世界环境中，尤其是在图像中具有巨大的图像处理潜力。提出了一种新的简化脉冲耦合神经网络（SPCNN）。这个新模型使用具有实际物理意义的像素强度作为输入参数，而不是原始SPCNN中的抽象网络参数。为了实现这一目标，我们尝试根据神经元的动态特性推导出SPCNN的动态阈值和内部活动的一般公式，进而推导出像素强度与抽象参数之间的关系。然后，将关系转化为客观优化问题，以获得合适的抽象参数。最后，