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Paquin, Valentin (2018) MEAN-FIELD ANALYSIS FOR MODEL-BASED SPIKING NETWORKS. Master's Thesis, University of Pittsburgh. (Unpublished)

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The human brain is composed of millions of neurons, firing spikes according to their membrane potentials. The difficulty in studying the brain exists partly because of the randomness property of neurons firing in a network. To understand more about the dynamics of a neuron’s firing rate, we choose to study a specific set of nonlinear dynamical equations that represent a neural network based on a spiking point of view with adaptation qualities. The dynamic membrane potential of a single neuron is a challenge to study since we can hardly know the number of spikes fired at a certain time. In this thesis, we use phase-plane analysis and more precisely mean-field analysis to address the random nature of the dynamic of model-based spiking networks. We find that the dynamics of neurons in a network offer exploitable and relevant information such as patterns of stable or unstable oscillations in certain circumstances.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Paquin, Valentinvap38@pitt.eduvap38
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairMao, zhi-hongzhm4@pitt.eduzhm4
Committee MemberKerestes, Robertrjk39@pitt.edurjk39
Committee MemberDallal, Ahmedahd12@pitt.eduahd12
Date: 20 September 2018
Date Type: Publication
Defense Date: 28 May 2018
Approval Date: 20 September 2018
Submission Date: 19 July 2018
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 60
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Electrical and Computer Engineering
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: Spiking network
Date Deposited: 20 Sep 2018 19:30
Last Modified: 20 Sep 2018 19:30

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