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Pattern segmentation with activity dependent natural frequency shift and sub-threshold resonance.

Shtrahman E, Zochowski M - Sci Rep (2015)

Bottom Line: Understanding the mechanisms underlying distributed pattern formation in brain networks and its content driven dynamical segmentation is an area of intense study.We investigate a theoretical mechanism for selective activation of diverse neural populations that is based on dynamically shifting cellular resonances in functionally or structurally coupled networks.We find that this mechanism is robust and suggest it as a general coding strategy that can be applied to any network with oscillatory nodes.

View Article: PubMed Central - PubMed

Affiliation: Applied Physics Program, University of Michigan - Ann Arbor 48109, USA.

ABSTRACT
Understanding the mechanisms underlying distributed pattern formation in brain networks and its content driven dynamical segmentation is an area of intense study. We investigate a theoretical mechanism for selective activation of diverse neural populations that is based on dynamically shifting cellular resonances in functionally or structurally coupled networks. We specifically show that sub-threshold neuronal depolarization from synaptic coupling or external input can shift neurons into and out of resonance with specific bands of existing extracellular oscillations, and this can act as a dynamic readout mechanism during information storage and retrieval. We find that this mechanism is robust and suggest it as a general coding strategy that can be applied to any network with oscillatory nodes.

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Related in: MedlinePlus

The influence of network topology on separation measures for connected network clusters.(a) Peak to peak separation ΔP (solid lines) increases for larger connectivity radius R. (B) Signal separation as a function of network topology (rewiring parameter p).
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f3: The influence of network topology on separation measures for connected network clusters.(a) Peak to peak separation ΔP (solid lines) increases for larger connectivity radius R. (B) Signal separation as a function of network topology (rewiring parameter p).

Mentions: We then investigated how the signal separation changes for two interconnected regions as a function of the network topology and the number of connections (Fig. 3). We vary the network topology from local to random coupling by changing the connection rewiring probability25. We find that this separation mechanism is effective across various network connectivity parameters. Both separation measures are only marginally influenced by the rewiring probability, showing a small decrease of separation for increasingly random networks. This is due to the fact that for more random networks there are relatively fewer connections within the functional subgroups while the subgroups are more tightly interconnected. At the same time, peak-to-peak separation increases for a higher radius of connectivity, as it provides additional input variance between the neurons within the heterogeneity and outside of it.


Pattern segmentation with activity dependent natural frequency shift and sub-threshold resonance.

Shtrahman E, Zochowski M - Sci Rep (2015)

The influence of network topology on separation measures for connected network clusters.(a) Peak to peak separation ΔP (solid lines) increases for larger connectivity radius R. (B) Signal separation as a function of network topology (rewiring parameter p).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4352860&req=5

f3: The influence of network topology on separation measures for connected network clusters.(a) Peak to peak separation ΔP (solid lines) increases for larger connectivity radius R. (B) Signal separation as a function of network topology (rewiring parameter p).
Mentions: We then investigated how the signal separation changes for two interconnected regions as a function of the network topology and the number of connections (Fig. 3). We vary the network topology from local to random coupling by changing the connection rewiring probability25. We find that this separation mechanism is effective across various network connectivity parameters. Both separation measures are only marginally influenced by the rewiring probability, showing a small decrease of separation for increasingly random networks. This is due to the fact that for more random networks there are relatively fewer connections within the functional subgroups while the subgroups are more tightly interconnected. At the same time, peak-to-peak separation increases for a higher radius of connectivity, as it provides additional input variance between the neurons within the heterogeneity and outside of it.

Bottom Line: Understanding the mechanisms underlying distributed pattern formation in brain networks and its content driven dynamical segmentation is an area of intense study.We investigate a theoretical mechanism for selective activation of diverse neural populations that is based on dynamically shifting cellular resonances in functionally or structurally coupled networks.We find that this mechanism is robust and suggest it as a general coding strategy that can be applied to any network with oscillatory nodes.

View Article: PubMed Central - PubMed

Affiliation: Applied Physics Program, University of Michigan - Ann Arbor 48109, USA.

ABSTRACT
Understanding the mechanisms underlying distributed pattern formation in brain networks and its content driven dynamical segmentation is an area of intense study. We investigate a theoretical mechanism for selective activation of diverse neural populations that is based on dynamically shifting cellular resonances in functionally or structurally coupled networks. We specifically show that sub-threshold neuronal depolarization from synaptic coupling or external input can shift neurons into and out of resonance with specific bands of existing extracellular oscillations, and this can act as a dynamic readout mechanism during information storage and retrieval. We find that this mechanism is robust and suggest it as a general coding strategy that can be applied to any network with oscillatory nodes.

Show MeSH
Related in: MedlinePlus