Phase synchrony facilitates binding and segmentation of natural images in a coupled neural oscillator network.
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.
Institute of Cognitive Science, University of Osnabrück Osnabrück, Germany.
Holger
H
Finger
Institute of Cognitive Science, University of Osnabrück Osnabrück, Germany ; Institute of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf Hamburg, Germany.
Peter
P
König
binding; natural image statistics; normative model; object label; oscillation; scene segmentation; synchronization; unsupervised learning