ArticleFrontiers in computational neuroscience2013
Phase synchrony facilitates binding and segmentation of natural images in a coupled neural oscillator network.
Article in Frontiers in computational neuroscience, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- A Novel Fractional-Order Chaotic Phase Synchronization Model for Visual Selection and Shifting.Entropy (Basel, Switzerland) · 2018Article
- Analysis of the dynamics of temporal relationships of neural activities using optical imaging data.Journal of computational neuroscience · 2017Article
- Modeling of Large-Scale Functional Brain Networks Based on Structural Connectivity from DTI: Comparison with EEG Derived Phase Coupling Networks and Evaluation of Alternative Methods along the Modeling Path.PLoS computational biology · 2016Article
- Correlated neuronal activity and its relationship to coding, dynamics and network architecture.Frontiers in computational neuroscience · 2014Article
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2 authors.
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Abstract
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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