Paquin, Valentin
(2018)
MEAN-FIELD ANALYSIS FOR MODEL-BASED SPIKING NETWORKS.
Master's Thesis, University of Pittsburgh.
(Unpublished)
This is the latest version of this item.
Abstract
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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Details
Item Type: |
University of Pittsburgh ETD
|
Status: |
Unpublished |
Creators/Authors: |
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ETD Committee: |
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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 |
URI: |
http://d-scholarship.pitt.edu/id/eprint/35009 |
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