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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

Resonance frequency shifts provide a mechanism for both functional and structural distributed network formation.(a) Sub-threshold input (arrows) driving a spatially distributed subset of the network (red circles), shifts the resonance frequency of those neurons creating a functional heterogeneity in the network, reflected in selective activation of functional clusters in the raster plot below. Learning mechanisms can strengthen these connections creating a distributed structural heterogeneity. (b) Distributed structural network heterogeneity (red circles) with additional coupling weight (thicker edges) is selectively activated based on the resonance frequency of sub-threshold input. Sub-threshold driving oscillations picture above raster plots are not to scale.
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f8: Resonance frequency shifts provide a mechanism for both functional and structural distributed network formation.(a) Sub-threshold input (arrows) driving a spatially distributed subset of the network (red circles), shifts the resonance frequency of those neurons creating a functional heterogeneity in the network, reflected in selective activation of functional clusters in the raster plot below. Learning mechanisms can strengthen these connections creating a distributed structural heterogeneity. (b) Distributed structural network heterogeneity (red circles) with additional coupling weight (thicker edges) is selectively activated based on the resonance frequency of sub-threshold input. Sub-threshold driving oscillations picture above raster plots are not to scale.

Mentions: Finally, we show that the neuronal representation can also be distributed throughout the network and the resonance frequency shift can act as a feature binding mechanism for such a distributed representation. We illustrate how such a representation can be formed based on external input and easily retrieved based on intrinsic network dynamics (Fig. 8). Initially, for a network with no additional coupling (i.e. no stored memory), a small additional sub-threshold current bias is given to a population of neurons spatially distributed in the network. The source of this additional current could be activation from sensory input. This current shifts the resonance frequency of neurons receiving the additional current into resonance with the driving oscillation (Fig. 8a). The distributed population is activated, forming a functionally correlated ensemble. Learning rules can strengthen these connections creating a structural heterogeneity that can later be reactivated with the same resonance frequency shift mechanism when no biasing current is present. We heuristically show the effect by artificially strengthening the connectivity between the same neuronal populations (Fig. 8b).


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

Shtrahman E, Zochowski M - Sci Rep (2015)

Resonance frequency shifts provide a mechanism for both functional and structural distributed network formation.(a) Sub-threshold input (arrows) driving a spatially distributed subset of the network (red circles), shifts the resonance frequency of those neurons creating a functional heterogeneity in the network, reflected in selective activation of functional clusters in the raster plot below. Learning mechanisms can strengthen these connections creating a distributed structural heterogeneity. (b) Distributed structural network heterogeneity (red circles) with additional coupling weight (thicker edges) is selectively activated based on the resonance frequency of sub-threshold input. Sub-threshold driving oscillations picture above raster plots are not to scale.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f8: Resonance frequency shifts provide a mechanism for both functional and structural distributed network formation.(a) Sub-threshold input (arrows) driving a spatially distributed subset of the network (red circles), shifts the resonance frequency of those neurons creating a functional heterogeneity in the network, reflected in selective activation of functional clusters in the raster plot below. Learning mechanisms can strengthen these connections creating a distributed structural heterogeneity. (b) Distributed structural network heterogeneity (red circles) with additional coupling weight (thicker edges) is selectively activated based on the resonance frequency of sub-threshold input. Sub-threshold driving oscillations picture above raster plots are not to scale.
Mentions: Finally, we show that the neuronal representation can also be distributed throughout the network and the resonance frequency shift can act as a feature binding mechanism for such a distributed representation. We illustrate how such a representation can be formed based on external input and easily retrieved based on intrinsic network dynamics (Fig. 8). Initially, for a network with no additional coupling (i.e. no stored memory), a small additional sub-threshold current bias is given to a population of neurons spatially distributed in the network. The source of this additional current could be activation from sensory input. This current shifts the resonance frequency of neurons receiving the additional current into resonance with the driving oscillation (Fig. 8a). The distributed population is activated, forming a functionally correlated ensemble. Learning rules can strengthen these connections creating a structural heterogeneity that can later be reactivated with the same resonance frequency shift mechanism when no biasing current is present. We heuristically show the effect by artificially strengthening the connectivity between the same neuronal populations (Fig. 8b).

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