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Exploring the Effects of Grouped Con...

Tumulty, Joseph Scott.

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Exploring the Effects of Grouped Connectivity on Network Synchronization in Simulated Neuron Networks.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Exploring the Effects of Grouped Connectivity on Network Synchronization in Simulated Neuron Networks./
作者:	Tumulty, Joseph Scott.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	84 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
Contained By:	Dissertations Abstracts International83-02B.
標題:	Biophysics. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28549582
ISBN:	9798534600377

Exploring the Effects of Grouped Connectivity on Network Synchronization in Simulated Neuron Networks.
Tumulty, Joseph Scott.

Exploring the Effects of Grouped Connectivity on Network Synchronization in Simulated Neuron Networks. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 84 p.

Source: Dissertations Abstracts International, Volume: 83-02, Section: B.

Thesis (Ph.D.)--Drexel University, 2021.

This item must not be sold to any third party vendors.

Neuronal connectivity in the cerebral cortex is far from random, with characteristics that point to a hierarchical design. Neurons form connections within columns, amorphous clusters and large communities. In this work I investigate computationally the effects of varying neuronal connectivity among groups of neurons on network synchronization for two different spatial distributions of groups: one where groups of neurons are arranged in columns in a grid and the other where neurons from different groups are spatially intermixed. The size of groups is varied as well as the orientation of the neurons (e.g. random positions, microcolumns). I compare cases by measuring the degree of neuronal spiking synchrony as a function of the number of connections per neuron and the degree of inter-cluster connectivity. I find that in all cases as the number of connections per neuron increases, there is an asynchronous to synchronous transition dependent on the number of neurons in the network and intrinsic parameters of the neuron biophysical model. In all cases I also find that with very low inter-cluster connectivity clusters have independent firing dynamics yielding a low degree of global synchrony. Most importantly, I find that for a high number of connections per neuron but intermediate inter-cluster connectivity, the networks with groups arranged in a grid are highly synchronized across the whole network while networks with intermixed groups enter a state of asynchronous high frequency activity. This phenomenon persists when the group sizes are decreased and when inhibitory neurons are added. Preliminary results suggests that a similar phenomenon occurs when neurons are grouped into microcolumns, giving these networks a distinct synchronization advantage over random networks and making them more robust against transitions to a high frequency asynchronous state. Topographical network measures such as the shortest connected distance between neurons are used to explain the variation in the degree of synchronization among the synchronized networks and to predict the transition to the high frequency asynchronous state.

ISBN: 9798534600377Subjects--Topical Terms:

518360
Biophysics.
Subjects--Index Terms:

Connectivity

Exploring the Effects of Grouped Connectivity on Network Synchronization in Simulated Neuron Networks.
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520 $a Neuronal connectivity in the cerebral cortex is far from random, with characteristics that point to a hierarchical design. Neurons form connections within columns, amorphous clusters and large communities. In this work I investigate computationally the effects of varying neuronal connectivity among groups of neurons on network synchronization for two different spatial distributions of groups: one where groups of neurons are arranged in columns in a grid and the other where neurons from different groups are spatially intermixed. The size of groups is varied as well as the orientation of the neurons (e.g. random positions, microcolumns). I compare cases by measuring the degree of neuronal spiking synchrony as a function of the number of connections per neuron and the degree of inter-cluster connectivity. I find that in all cases as the number of connections per neuron increases, there is an asynchronous to synchronous transition dependent on the number of neurons in the network and intrinsic parameters of the neuron biophysical model. In all cases I also find that with very low inter-cluster connectivity clusters have independent firing dynamics yielding a low degree of global synchrony. Most importantly, I find that for a high number of connections per neuron but intermediate inter-cluster connectivity, the networks with groups arranged in a grid are highly synchronized across the whole network while networks with intermixed groups enter a state of asynchronous high frequency activity. This phenomenon persists when the group sizes are decreased and when inhibitory neurons are added. Preliminary results suggests that a similar phenomenon occurs when neurons are grouped into microcolumns, giving these networks a distinct synchronization advantage over random networks and making them more robust against transitions to a high frequency asynchronous state. Topographical network measures such as the shortest connected distance between neurons are used to explain the variation in the degree of synchronization among the synchronized networks and to predict the transition to the high frequency asynchronous state.
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