Leaky Integrate-and-Fire Network Model in Approximating Local Field Potential
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Wang, Chuyu
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Abstract
The local field potential (LFP) serves as a valuable indicator of neural network activity,
reflecting the intricate flow of information within the brain. However, the interpretation of LFP
signals can be challenging due to the complex contributions from multiple neural sources. This
thesis explores the efficacy of the leaky integrate-and-fire (LIF) network model in approximating
LFP signals, offering a computationally efficient yet adaptable framework for modeling brain
dynamics. Through a combination of literature reviews, software implementation, and validation
procedures, this research investigates the LIF model's potential contributions to the field of
neurophysiology.
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Undergraduate Research Option Thesis