Research On The Neural Network Model Based On Lateral Inhibition

Lateral inhibition is one of the basic principles in neural information processing systems which exist in biological neural systems. It can be used in image processing to make the result more suitable to human visual system. Many neural network models have been proposed based on the biological physiological characteristics. This paper will make a research on the neural network model based on lateral inhibition of the visual system .First, the pulse coupled neural network (PCNN), which is based on the biological system, has been researched. In image processing, PCNN is used as a single 2-D local connection network, where nerve cell and pixel are one to one correspondence. This paper discusses the parameters of PCNN and its application to image segmentation, image enhancement and edge detection. Advantages and disadvantages are compared with traditional methods to study the visual system model based on biological characteristics. The model based on biological vision systems in image processing runs faster, performs more natural results, and extracts information well from images with good grayscale expression.Second, classification and discussion of biological lateral inhibition model have been conducted. Two new methods have been proposed according to the weakness of the lateral inhibition model used in image processing: one is added a possibility measure factor; the other is combined with the adaptive filter. Experiments demonstrate the feasibility of the two methods. Based on the weakness of PCNN in image processing, a new model-the combination of lateral inhibition mechanism and the PCNN model is proposed. Simulation has been made to compare with the pure PCNN model. The results have more clearly contours and better connectivity.Finally, a location tracking model based on Integrate-and-Fire (IF) mechanism using neural network ensemble of lateral inhibition has been proposed. A new iterative training algorithm PITS (Progressive iterative training scheme) is used for parameters learning. Using information center (IC) to store the results of each training session. To ensure convergence , the tracking weight adjustment formula is given by comparing the results of the error function. The IF model which is simplified by H-H model is used. The lateral inhibition is simulated meanwhile the purpose of position tracking is achieved, and the tracking accuracy and speed have been increased because of the new learning algorithm.