Randomly reconfigurable Cellular Neural Network

Biological networks involve both regular and random connections. Moreover they employ more than one type of cells. Being widely used in bio-inspired systems, Cellular Neural Networks are practical to implement large networks due to their regularly defined connections between unit processors. However, this perfect regularity of the structure does not always match with applications. Although it is widely selected for retina like demonstrations itself, there is an absence of CNN for using it in other bio-inspired systems: an ordinary CNN has only one type of unit processor in one layer. However, sensory data processing in nature mainly depend on the collaboration of distinct dynamics. Neural mass models are suggested to mimic the joint effort of distinct types of neurons and they are widely used to simulate and understand brain activity. The regularity of an ordinary CNN benefits in implementation of the network. While protecting this simplicity, in this work, we propose a method to build a single layer cellular neural network that can perform a Wilson-Cowan like neural population model.