Phase synchrony facilitates binding and segmentation of natural images in a coupled neural oscillator network
|binding; CODE; COMPUTATIONS; Mathematical & Computational Biology; MODEL; natural image statistics; Neurosciences; Neurosciences & Neurology; normative model; object label; oscillation; RESPONSES; scene segmentation; SMALL-WORLD; STATISTICS; synchronization; unsupervised learning; VISUAL-CORTEX
|FRONTIERS MEDIA SA
|FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
Synchronization has been suggested as a mechanism of binding distributed feature representations facilitating segmentation of visual stimuli. Here we investigate this concept based on unsupervised learning using natural visual stimuli. We simulate dual-variable neural oscillators with separate activation and phase variables. The binding of a set of neurons is coded by synchronized phase variables. The network of tangential synchronizing connections learned from the induced activations exhibits small-world properties and allows binding even over larger distances. We evaluate the resulting dynamic phase maps using segmentation masks labeled by human experts. Our simulation results show a continuously increasing phase synchrony between neurons within the labeled segmentation masks. The evaluation of the network dynamics shows that the synchrony between network nodes establishes a relational coding of the natural image inputs. This demonstrates that the concept of binding by synchrony is applicable in the context of unsupervised learning using natural visual stimuli.
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