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A new class of methods for functional connectivity estimation

Abstract

Measuring functional connectivity from neural recordings is important in understanding

processing in cortical networks. The covariance-based methods are the current

golden standard for functional connectivity estimation. However, the link between the

pair-wise correlations and the physiological connections inside the neural network is unclear.

Therefore, the power of inferring physiological basis from functional connectivity estimation

is limited. To build a stronger tie and better understand the relationship between functional

connectivity and physiological neural network, we need (1) a realistic model to simulate

different types of neural recordings with known ground truth for benchmarking; (2) a new

functional connectivity method that produce estimations closely reflecting the physiological

basis.

In this thesis, (1) I tune a spiking neural network model to match with human sleep

EEG data, (2) introduce a new class of methods for estimating connectivity from different

kinds of neural signals and provide theory proof for its superiority, (3) apply it to simulated

fMRI data as an application.

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