Inhibition as a determinant of activity and criticality in dynamical networks
Abstract:
Inhibition plays a crucial role in determining the activity and stability of neural networks. In this work, we study the effect of inhibition on the dynamics of random threshold networks (RTNs), a simplified model of neural networks. We show that inhibition can control the level of activity in the network and can drive the system to a critical state. We find that the critical point is characterized by a balance between excitation and inhibition, and that small changes in the inhibition strength can lead to drastic changes in the network dynamics. Our results suggest that inhibition is a key mechanism for maintaining neural networks in a critical state, which is believed to be optimal for information processing.