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A new class of methods for functional connectivity estimation
- Lin, Wutu
- Advisor(s): Sejnowski, Terrence J
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.
Main Content
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