A spatial deep network architecture for brain decoding

Machine Learning;Brain Decoding;Deep Learning

Habeeb, Haroun ; Koyejo ; Oluwasanmi

关键词: Machine Learning;    Brain Decoding;    Deep Learning;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/104898/HABEEB-THESIS-2019.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

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.