This paper studies dynamic binary neural networks characterized by signum activation function, local connection parameters and integer threshold paremeters. The DBNN is constructed by applying delayed feedback to the binary neural networks. The network can generate various periodic orbits. The dynamics is simplified into a digital return map on a set of lattice points. We analyze the dynamics by replacing The DBNN with a simple class network in this paper. We consider the relationship between cellular automata and DBNN. Calculating feature quantities, we investigate the relationship between a simple class of CA and DBNN with local connection. Analysis of the DBNN is important not only as fundamental nonlinear problems but also for engineering applications
