Critical temperature of the transiently chaotic neural network

The dynamical behaviour of an optimizing neural network is closely related to its parameters. For the transiently chaotic neural network (TCNN), the temperature, i.e., self-feedback weighting, is an important parameter for the network performance. While a high temperature is required to investigate chaotic dynamics, a low temperature is preferred for combinatorial optimization application. In this article, we derived this critical temperature of the TCNN analytically and illustrated its validity using computer simulation.