Application of particle swarm system as a novel parameter optimization technique on spatiotemporal retina model

Abstract

Center-surround spatiotemporal (ST) filter is a powerful tool to simulate the spatial and temporal properties of retina ganglion cells and encode visual information with electric spikes. This paper introduces the application of particle swarm optimization (PSO) algorithm to tune the parameters in the retina model consisting of a ST filter module and a back-propagation (BP) neural network module. Images are converted into electric spikes by the ST filters whose outputs are then fed into the BP neural network to reconstruct the output images. The parameters of the ST filters determine the electric spike sequences as well as the output image from the BP network. In order to get the expected output images, we employ PSO to iteratively tune the parameters. Euclidean distance between output and input image is used as scalar criteria to optimize the ST filter. The tuning process stops until the similarity between output and input images no longer improves. The results show that 62.3% of the images trained by PSO have better output image quality and less iteration time compared with those trained by the current evolution strategy (ES).