Stability analysis of the brain-state-in-a-box (BSB) neural networks with delay

In this paper we discuss the dynamic properties of a family of brain-state-in-a-box (BSB) models with delay. We have performed a detailed stability analysis of this network and found that under proper assumptions when the connection weight matrix of this network is strongly row diagonal dominant or non-symmetric, the relationships among the extreme point, equilibrium points and stable points in this network can be obtained. Theoretical analysis demonstrates that the BSB with delay performs much better than the original BSB in updating to an extreme point.