A spatial deep network architecture for brain decoding
Habeeb, Haroun
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https://hdl.handle.net/2142/104898
Description
Title
A spatial deep network architecture for brain decoding
Author(s)
Habeeb, Haroun
Issue Date
2019-04-22
Director of Research (if dissertation) or Advisor (if thesis)
Koyejo, Oluwasanmi
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Machine Learning
Brain Decoding
Deep Learning
Abstract
We propose the Fixed Grouping Layer (FGL); a novel feedforward layer designed to incorporate structured smoothness in a deep learning model. FGL achieves this goal by connecting nodes across layers based on spatial similarity. The inductive bias of structured smoothness implemented by FGL is motivated by applications such as brain image decoding, i.e., predicting behavior based on brain images, where scientific prior knowledge suggests that brain responses conditioned on behaviour are smoothed. Experimental results on simulated and real data is provided. Our proposed model architecture performs better than conventional neural network architectures.
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