Phase synchrony facilitates binding and segmentation of natural images in a coupled neural oscillator network

Holger Finger, Peter König
<span title="">2014</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="" style="color: black;">Frontiers in Computational Neuroscience</a> </i> &nbsp;
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
more &raquo; ... mall-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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.3389/fncom.2013.00195</a> <a target="_blank" rel="external noopener" href="">pmid:24478685</a> <a target="_blank" rel="external noopener" href="">pmcid:PMC3902207</a> <a target="_blank" rel="external noopener" href="">fatcat:gqovw4eqvreklp4ybhoxmghory</a> </span>
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