  While traditional feed-forward filter models can reproduce the rate responses
of retinal ganglion neurons to simple stimuli, they cannot explain why
synchrony between spikes is much higher than expected by Poisson firing [6],
and can be sometimes rhythmic [25, 16]. Here we investigate the hypothesis that
synchrony in periodic retinal spike trains could convey contextual information
of the visual input, which is extracted by computations in the retinal network.
We propose a computational model for image segmentation consisting of a
Kuramoto model of coupled oscillators whose phases model the timing of
individual retinal spikes. The phase couplings between oscillators are shaped
by the stimulus structure, causing cells to synchronize if the local contrast
in their receptive fields is similar. In essence, relaxation in the oscillator
network solves a graph clustering problem with the graph representing feature
similarity between different points in the image. We tested different model
versions on the Berkeley Image Segmentation Data Set (BSDS). Networks with
phase interactions set by standard representations of the feature graph
(adjacency matrix, Graph Laplacian or modularity) failed to exhibit
segmentation performance significantly over the baseline, a model of
independent sensors. In contrast, a network with phase interactions that takes
into account not only feature similarities but also geometric distances between
receptive fields exhibited segmentation performance significantly above
baseline.
