Microscopic instability in recurrent neural networks

Abstract

In a manner similar to the molecular chaos that underlies the stable thermodynamics of gases, neuronal system may exhibit microscopic instability in individual neuronal dynamics while a macroscopic order of the entire population possibly remains stable. In this study, we analyze the microscopic stability of a network of neurons whose macroscopic activity obeys stable dynamics, expressing either monostable, bistable, or periodic state. We reveal that the network exhibits a variety of dynamical states for microscopic instability residing in given stable macroscopic dynamics. The presence of a variety of dynamical states in such a simple random network implies more abundant microscopic fluctuations in real neural networks, which consist of more complex and hierarchically structured interactions.

9 pages, 12 figures

References in corpus (5)

• Statistical physics of social dynamics
• Robust dynamic classes revealed by measuring the response function of a social system
• Global and local synchrony of coupled neurons in small-world networks
• Pulsed chaos synchronization in networks with adaptive couplings
• State Concentration Exponent as a Measure of Quickness in Kauffman-type Networks