Limits...
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.

Show MeSH

Related in: MedlinePlus

Resonance Frequency shift mechanism can selectively activate multiple competing heterogeneities through lateral inhibition.(a) Example raster plot shows selective switching between heterogeneities for a driving frequency of 25.5 Hz. Neurons with IDs 50 to 150 belong to one heterogeneity, while neurons with IDs 250 to 350 are in a second heterogeneity. Between 5 and 15 seconds of the simulation heterogeneity 1 is shifted into resonance with sub-threshold input applied a randomly chosen subset of 50% of neurons in that heterogeneity. Similarly, Heterogeneity 2 is shifted into resonance with sub-threshold input, between 20 and 30 seconds of the simulation. As a control, a region outside of both heterogeneities (IDs 150 to 250) receives identical sub-threshold input between 35 and 45 seconds in the simulation, and results in no enhanced firing rate. (b) Corresponding firing rate responses of regions of the network to sub-threshold input shows multiple heterogeneities can be selectively activated. (c) Targeted inhibition can dynamically and rapidly switch activation between heterogeneities. Heterogeneity 1 is activated by sub-threshold stimulation of 50% of neurons belonging to that heterogeneity; the x-axis denotes the percentage of stimulated cells in second heterogeneity. The activation of heterogeneities switches rapidly as number of stimulated neurons in heterogeneity 2 grows. (d) Same as (c) averaged over four network representations, and error bars represent 1 standard error of the mean. Example shown for driving frequency of 21.5 Hz.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4352860&req=5

f7: Resonance Frequency shift mechanism can selectively activate multiple competing heterogeneities through lateral inhibition.(a) Example raster plot shows selective switching between heterogeneities for a driving frequency of 25.5 Hz. Neurons with IDs 50 to 150 belong to one heterogeneity, while neurons with IDs 250 to 350 are in a second heterogeneity. Between 5 and 15 seconds of the simulation heterogeneity 1 is shifted into resonance with sub-threshold input applied a randomly chosen subset of 50% of neurons in that heterogeneity. Similarly, Heterogeneity 2 is shifted into resonance with sub-threshold input, between 20 and 30 seconds of the simulation. As a control, a region outside of both heterogeneities (IDs 150 to 250) receives identical sub-threshold input between 35 and 45 seconds in the simulation, and results in no enhanced firing rate. (b) Corresponding firing rate responses of regions of the network to sub-threshold input shows multiple heterogeneities can be selectively activated. (c) Targeted inhibition can dynamically and rapidly switch activation between heterogeneities. Heterogeneity 1 is activated by sub-threshold stimulation of 50% of neurons belonging to that heterogeneity; the x-axis denotes the percentage of stimulated cells in second heterogeneity. The activation of heterogeneities switches rapidly as number of stimulated neurons in heterogeneity 2 grows. (d) Same as (c) averaged over four network representations, and error bars represent 1 standard error of the mean. Example shown for driving frequency of 21.5 Hz.

Mentions: Inhibition can play additional, critical role within this framework. For the resonance frequency shift mechanism to be a general coding strategy, it has to be successfully applied to separate multiple competing neuronal representations. We show that separate heterogeneities can be selectively activated and dynamically switched through the use of targeted lateral inhibition (see methods). Excitatory neurons within competing heterogeneities mutually inhibit each other so that activation of one heterogeneity shifts the resonance of another heterogeneity away from the frequency of driving oscillation. Figure 7 shows an example network with two heterogeneities of identically enhanced synaptic coupling (σ = 4). In this case, the heterogeneities do not overlap and share basic excitatory connectivity (σ = 1) between each other and across the rest of the network. In addition, there are targeted inhibitory connections originating at one heterogeneity and targeting neurons in the other heterogeneity. Sub-threshold input applied to a subset of neurons within each heterogeneity selectively enhances the firing rate in that heterogeneity, but no enhanced firing occurs when input is applied to regions of the network outside of either heterogeneity (Fig. 7a,b). Thus, the heterogeneities can be selectively and dynamically activated. Similar strategy (targeted inhibition) could possibly be applied to multiple and overlapping representations.


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

Shtrahman E, Zochowski M - Sci Rep (2015)

Resonance Frequency shift mechanism can selectively activate multiple competing heterogeneities through lateral inhibition.(a) Example raster plot shows selective switching between heterogeneities for a driving frequency of 25.5 Hz. Neurons with IDs 50 to 150 belong to one heterogeneity, while neurons with IDs 250 to 350 are in a second heterogeneity. Between 5 and 15 seconds of the simulation heterogeneity 1 is shifted into resonance with sub-threshold input applied a randomly chosen subset of 50% of neurons in that heterogeneity. Similarly, Heterogeneity 2 is shifted into resonance with sub-threshold input, between 20 and 30 seconds of the simulation. As a control, a region outside of both heterogeneities (IDs 150 to 250) receives identical sub-threshold input between 35 and 45 seconds in the simulation, and results in no enhanced firing rate. (b) Corresponding firing rate responses of regions of the network to sub-threshold input shows multiple heterogeneities can be selectively activated. (c) Targeted inhibition can dynamically and rapidly switch activation between heterogeneities. Heterogeneity 1 is activated by sub-threshold stimulation of 50% of neurons belonging to that heterogeneity; the x-axis denotes the percentage of stimulated cells in second heterogeneity. The activation of heterogeneities switches rapidly as number of stimulated neurons in heterogeneity 2 grows. (d) Same as (c) averaged over four network representations, and error bars represent 1 standard error of the mean. Example shown for driving frequency of 21.5 Hz.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f7: Resonance Frequency shift mechanism can selectively activate multiple competing heterogeneities through lateral inhibition.(a) Example raster plot shows selective switching between heterogeneities for a driving frequency of 25.5 Hz. Neurons with IDs 50 to 150 belong to one heterogeneity, while neurons with IDs 250 to 350 are in a second heterogeneity. Between 5 and 15 seconds of the simulation heterogeneity 1 is shifted into resonance with sub-threshold input applied a randomly chosen subset of 50% of neurons in that heterogeneity. Similarly, Heterogeneity 2 is shifted into resonance with sub-threshold input, between 20 and 30 seconds of the simulation. As a control, a region outside of both heterogeneities (IDs 150 to 250) receives identical sub-threshold input between 35 and 45 seconds in the simulation, and results in no enhanced firing rate. (b) Corresponding firing rate responses of regions of the network to sub-threshold input shows multiple heterogeneities can be selectively activated. (c) Targeted inhibition can dynamically and rapidly switch activation between heterogeneities. Heterogeneity 1 is activated by sub-threshold stimulation of 50% of neurons belonging to that heterogeneity; the x-axis denotes the percentage of stimulated cells in second heterogeneity. The activation of heterogeneities switches rapidly as number of stimulated neurons in heterogeneity 2 grows. (d) Same as (c) averaged over four network representations, and error bars represent 1 standard error of the mean. Example shown for driving frequency of 21.5 Hz.
Mentions: Inhibition can play additional, critical role within this framework. For the resonance frequency shift mechanism to be a general coding strategy, it has to be successfully applied to separate multiple competing neuronal representations. We show that separate heterogeneities can be selectively activated and dynamically switched through the use of targeted lateral inhibition (see methods). Excitatory neurons within competing heterogeneities mutually inhibit each other so that activation of one heterogeneity shifts the resonance of another heterogeneity away from the frequency of driving oscillation. Figure 7 shows an example network with two heterogeneities of identically enhanced synaptic coupling (σ = 4). In this case, the heterogeneities do not overlap and share basic excitatory connectivity (σ = 1) between each other and across the rest of the network. In addition, there are targeted inhibitory connections originating at one heterogeneity and targeting neurons in the other heterogeneity. Sub-threshold input applied to a subset of neurons within each heterogeneity selectively enhances the firing rate in that heterogeneity, but no enhanced firing occurs when input is applied to regions of the network outside of either heterogeneity (Fig. 7a,b). Thus, the heterogeneities can be selectively and dynamically activated. Similar strategy (targeted inhibition) could possibly be applied to multiple and overlapping representations.

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