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Collective Dynamics of Excitable Tree Networks

Khaledi Nasab, Ali
http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1562669848013115

Abstract Details

2019, Doctor of Philosophy (PhD), Ohio University, Physics and Astronomy (Arts and Sciences).
We study the collective dynamics of diffusively coupled excitable elements in small tree networks with regular and random connectivity, which model sensory neurons with branched myelinated distal terminals. These neurons possess dendritic trees with myelinated branches and with nodes of Ranvier at every branching points. They may show spontaneous noisy periodic spiking. Examples of such neurons include touch receptors, muscle spindles afferents and some electroreceptors. A mathematical model of such a neuron is a system of excitable elements coupled on a tree network. We show that the mechanism of periodic firing is rooted in the synchronization of local activity of individual nodes, even though peripheral nodes may receive random independent inputs. We developed a theory that predicts the collective spiking activity in physiologically-relevant strong coupling limit. The structural variability in random tree networks translates into collective network dynamics leading to a wide range of firing rates and coefficients of variations, which is most pronounced in the strong coupling regime. We studied signal detection in regular and random trees. Our results indicate that the highest sensitivity occurs in specific optimum values of the input current for any given tree network. In the presence of a time-dependent uniform stimulus, we have shown that the highest information carried by spikes of the central node of a tree about the stimulus is attained for the strong coupling, even though the firing rate is at maximum for smaller values of coupling strength. Finally, we studied the effect of inhomogeneous inputs on the collective response of tree networks and showed that it leads to additional variability of collective firing.
Alexander Neiman (Advisor)
Jung Peter (Committee Member)
Day Mitchell (Committee Member)
Young Todd (Committee Member)
179 p.
Biophysics; Nanoscience; Physics
Excitable tree networks; Random trees; Hodgkin-Huxley; Collective dynamics; Diffusively coupled excitable elements; Frankenhaeuser-Huxley; Stochastic dynamics; Deterministic dynamics; Strongly coupled stochastic excitable elements

Recommended Citations

Citations

  • Khaledi Nasab, A. (2019). Collective Dynamics of Excitable Tree Networks [Doctoral dissertation, Ohio University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1562669848013115

    APA Style (7th edition)

  • Khaledi Nasab, Ali. Collective Dynamics of Excitable Tree Networks. 2019. Ohio University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1562669848013115.

    MLA Style (8th edition)

  • Khaledi Nasab, Ali. "Collective Dynamics of Excitable Tree Networks." Doctoral dissertation, Ohio University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1562669848013115

    Chicago Manual of Style (17th edition)

ohiou1562669848013115
593
© 2019, all rights reserved.
This open access ETD is published by Ohio University and OhioLINK.