Neurally-based algorithms for image processing

One of the more difficult problems in image processing is segmentation. The human brain has an ability that is unmatched by any current technology for breaking down the world into distributed features and reconstructing them into distinct objects. Neurons encode information both in the number of spikes fired in a given time period, which indicates the strength with which a given local feature is present, and in the temporal code or relative timing of the spike, indicating whether the individual features are part of the same or different objects. Neurons that respond to contiguous stimuli produce synchronous oscillations, while those that are not fire independently. Thus, neural synchrony could be used as a tag for each pixel in an image indicating to which object it belongs. We have developed a simulation based on the primary visual cortex. We found that neurons that respond to the same object oscillate synchronously while those that respond to different objects fire independently.