A recent experiment showed that neurons in the primary auditory cortex of the monkey do not change their mean firing rate during an ongoing tone stimulus. The only change was an enhanced correlation among the individual spike trains during the tone. We show that there is an easy way to extract this coherence information in the cortical cell population by projecting the spike trains through depressing synapses onto a postsynaptic neuron.

We propose a model of coupled oscillators with noise that performs segmentation of stimuli using a set of stored images, each consisting of objects and a background. The oscillators' amplitudes encode the spatial and featural distribution of the external stimulus. The coherence of their phases signifies their belonging to the same object. In the learning stage, the couplings between phases are modified in a Hebb-like manner. By mean-field analysis and simulations, we show that an external stimulus whose local features resemble those of one or several of the stored objects generates a selective phase coherence that represents the stored pattern of segmentation.