On Conditions for Intermittent Search in Self-organizing Neural Networks

Self-organizing neural networks (SONN) driven by softmax weight renormalization are capable of finding high quality solutions of difficult assignment optimization problems. The renormalization is shaped by a temperature parameter - as the system cools down the assignment weights become increasingly crisp. It has been recently observed that there exists a critical temperature setting at which SONN is capable of powerful intermittent search through a multitude of high quality solutions represented as meta-stable states of SONN adaptation dynamics. The critical temperature depends on the problem size. It has been hypothesized that the intermittent search by SONN can occur only at temperatures close to the first (symmetry breaking) bifurcation temperature of the autonomous renormalization dynamics. In this paper we provide a rigorous support for the hypothesis by studying stability types of SONN renormalization equilibria.

• Type: Conference
• Year: 2007
• Where: MICAI
• Authors: Peter Tino
• Keywords: Artificial Intelligence, Critical Temperature, MICAI 2007, Renormalization, Softmax Weight Renormalization