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

Total activity within heterogeneity shows separation in frequency content and increased phase coherence with the sub-threshold driving oscillation when in resonance.(a) Difference in power of total network activity (heterogeneity – outside heterogeneity) shows a peak in the power spectrum for resonant frequency of driving oscillation (marked with * in fig. 1). (b) Difference in Mean Phase Coherence between sub-threshold oscillatory input and the activity signal formed within (blue) and outside (green) the heterogeneity.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f4: Total activity within heterogeneity shows separation in frequency content and increased phase coherence with the sub-threshold driving oscillation when in resonance.(a) Difference in power of total network activity (heterogeneity – outside heterogeneity) shows a peak in the power spectrum for resonant frequency of driving oscillation (marked with * in fig. 1). (b) Difference in Mean Phase Coherence between sub-threshold oscillatory input and the activity signal formed within (blue) and outside (green) the heterogeneity.

Mentions: The above results indicate that we see a robust separation in the frequency response of the neurons forming the network heterogeneity from the rest of the network. It has been shown that hippocampal memory formation both in animals and humans is accompanied by increased power in the theta band oscillation as well as phase coherence of neuronal activity with oscillations at that frequency212223. We hypothesize that the experimentally observed increase in power, for example of theta during memory consolidation, as well as the increased phase coherence of neuronal activities with that band, can be explained by our resonance readout mechanism, as it is well established that the resonating oscillators lock to driving oscillatory signals27. To that effect we investigated the difference in the power spectrum of the average activity within and outside of network heterogeneities. We compared the power spectrum of the average activity within the network heterogeneity to the rest of the network and found a notable increase in power when the driving oscillation is in resonance with the network heterogeneity (Fig. 4a). To better understand the increase of power around the driving frequency, we measured changes in the phase locking between the neurons within and outside of the heterogeneity as a function of driving frequency. To quantify the phase locking we computed the instantaneous phase of the network activity. Using the phase difference between the network activity and the sub-threshold driving oscillation, we calculated the Mean Phase Coherence (MPC)28, and found a substantial increase in mean phase coherence between the driving oscillation and the heterogeneity at its resonant peak (Fig. 4b). Here we depicted the mean phase coherence as a function of the driving frequency for the heterogeneity (blue line), for the rest of the network (green line), and for the difference between the two (black line). We observe that the mean phase coherence between the network firing and the driving oscillation is relatively highest at the raising phase of the network resonance curve peaks for both the heterogeneity and the rest of the network. Thus the proposed resonance frequency shift mechanism separates out the cell activities within a network heterogeneity for a significant range of driving frequencies in both their firing rate and phase coherence with ongoing oscillations. It is interesting to note that the mean phase coherence drops rapidly on the falling phase of the resonance curve. This again can be attributed to the large heterogeneity in cellular resonance frequencies, with relatively few neurons driving the network-wide response.


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

Shtrahman E, Zochowski M - Sci Rep (2015)

Total activity within heterogeneity shows separation in frequency content and increased phase coherence with the sub-threshold driving oscillation when in resonance.(a) Difference in power of total network activity (heterogeneity – outside heterogeneity) shows a peak in the power spectrum for resonant frequency of driving oscillation (marked with * in fig. 1). (b) Difference in Mean Phase Coherence between sub-threshold oscillatory input and the activity signal formed within (blue) and outside (green) the heterogeneity.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f4: Total activity within heterogeneity shows separation in frequency content and increased phase coherence with the sub-threshold driving oscillation when in resonance.(a) Difference in power of total network activity (heterogeneity – outside heterogeneity) shows a peak in the power spectrum for resonant frequency of driving oscillation (marked with * in fig. 1). (b) Difference in Mean Phase Coherence between sub-threshold oscillatory input and the activity signal formed within (blue) and outside (green) the heterogeneity.
Mentions: The above results indicate that we see a robust separation in the frequency response of the neurons forming the network heterogeneity from the rest of the network. It has been shown that hippocampal memory formation both in animals and humans is accompanied by increased power in the theta band oscillation as well as phase coherence of neuronal activity with oscillations at that frequency212223. We hypothesize that the experimentally observed increase in power, for example of theta during memory consolidation, as well as the increased phase coherence of neuronal activities with that band, can be explained by our resonance readout mechanism, as it is well established that the resonating oscillators lock to driving oscillatory signals27. To that effect we investigated the difference in the power spectrum of the average activity within and outside of network heterogeneities. We compared the power spectrum of the average activity within the network heterogeneity to the rest of the network and found a notable increase in power when the driving oscillation is in resonance with the network heterogeneity (Fig. 4a). To better understand the increase of power around the driving frequency, we measured changes in the phase locking between the neurons within and outside of the heterogeneity as a function of driving frequency. To quantify the phase locking we computed the instantaneous phase of the network activity. Using the phase difference between the network activity and the sub-threshold driving oscillation, we calculated the Mean Phase Coherence (MPC)28, and found a substantial increase in mean phase coherence between the driving oscillation and the heterogeneity at its resonant peak (Fig. 4b). Here we depicted the mean phase coherence as a function of the driving frequency for the heterogeneity (blue line), for the rest of the network (green line), and for the difference between the two (black line). We observe that the mean phase coherence between the network firing and the driving oscillation is relatively highest at the raising phase of the network resonance curve peaks for both the heterogeneity and the rest of the network. Thus the proposed resonance frequency shift mechanism separates out the cell activities within a network heterogeneity for a significant range of driving frequencies in both their firing rate and phase coherence with ongoing oscillations. It is interesting to note that the mean phase coherence drops rapidly on the falling phase of the resonance curve. This again can be attributed to the large heterogeneity in cellular resonance frequencies, with relatively few neurons driving the network-wide response.

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