Template Optimization in Cellular Neural Networks Using Gradient Based Approaches

Abstract

Cellular neural networks were used with success in the past decades and helped laying the foundations of neural net-work applications in image processing. In the last few years convolutional networks have appeared and helped in the solution of complex practical problems. Meanwhile programming templates of cellular neural networks were designed by analytical methods, gradient based optimization is applied popularly in convolutional networks. In this paper we will demonstrate how these methods can be exploited using cellular networks and how they can be used to implement classification and feature extraction tasks, both with standard and memristive cell dynamics.