An oscillation model of auditory stream segregation

Auditory segmentation is critical for complex auditory pattern processing. We present a neural network framework for auditory pattern segmentation. The network is a laterally coupled two-dimensional neural oscillators with a global inhibitor. One dimension represents time and another one represents frequency. We show that this architecture can in real time group auditory features into a segment by phase synchrony and segregate different segments by desynchronization. The network demonstrates the phenomenon that auditory stream segregation critically depends on the rate of presentation. The neuroplausibility of the model is discussed