Chapter 4 - Patterns of Dynamic Activity and Timing in Neural Network Processing

Abstract

The chapter discusses topics regarding the dynamic behavior of neural networks as they oscillate and produce specific timing patterns in their activity. A network of simple processing units is capable of producing prolonged self-sustained oscillations and even chaotic behavior. Modulation of a controlled parameter causes the temporal dynamics to increase in complexity until chaos is reached. The chapter focuses on how an external stimulus—a pattern—can be applied to a chaotic network, resulting in a simpler, limit cycle attractor, which can be recognized in a pattern-to-oscillation map. As random networks tend to have only one observed dynamic attractor, a weight perturbation schedule has been designed to develop multiple dynamic attractors from different initial states of the network. The aim is to create different basins of attraction for different patterns or pattern groups. A tremendous flexibility is observed not only in evoked attractors (usually oscillations) but also in their basins of attraction—the collections of states that lead to the same attractor.