A neural network for scene segmentation by temporal coding

We propose a neural network for object definition and scene segmentation via temporal signal correlations. The network consists of model neurons with a dynamic threshold and with two functionally different types of synapses. Activities of model neurons responding to a given object in the input image are synchronized, and desynchronized with respect to the activities of neural assemblies that respond to other objects. The present network can separate up to eight identical objects in a visual scene. The simulation results support the hypothesis that synchronous assembly activity in the visual system serves as a functional principle for feature linking, object definition, and scene segmentation.