Investigating the synchronization properties of a two-coupled chaotic neural network

Inspired by biological neural systems such as the visual pathway, the dynamics of discrete-time recurrent neural network with non-lateral connections has been shown to exhibit chaos. Additionally, it is known that linked nonlinear chaotic systems can achieve synchronization. In this project we build upon these two concepts to investigate whether a two-coupled neural network, one of which possesses a chaotic phase space can induce chaos into another neural network with a non-chaotic phase space. These networks are linked by two non-reciprocal laterally connected weights allowing them to synchronize. Based on our calculations, where one of the networks is initiated with a chaotic phase space, and the other in a periodic state, we find that the non-chaotic neural network can synchronize with a chaotic neural network. Thus chaotic neural network synchronization can be achieved in a two-coupled model.