Title
Deep Learning Architecture With Dynamically Programmed Layers For Brain Connectome Prediction
Abstract
This paper explores the idea of using deep neural network architecture with dynamically programmed layers for brain connectome prediction problem. Understanding the brain connectome structure is a very interesting and a challenging problem. It is critical in the research for epilepsy and other neuropathological diseases. We introduce a new deep learning architecture that exploits the spatial and temporal nature of the neuronal activation data. The architecture consists of a combination of Convolutional layer and a Recurrent layer for predicting the connectome of neurons based on their time-series of activation data. The key contribution of this paper is a dynamically programmed layer that is critical in determining the alignment between the neuronal activations of pair-wise combinations of neurons.
Publication Date
8-10-2015
Publication Title
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Volume
2015-August
Number of Pages
1205-1214
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1145/2783258.2783399
Copyright Status
Unknown
Socpus ID
84954104243 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/84954104243
STARS Citation
Veeriah, Vivek; Durvasula, Rohit; and Qi, Guo Jun, "Deep Learning Architecture With Dynamically Programmed Layers For Brain Connectome Prediction" (2015). Scopus Export 2015-2019. 1765.
https://stars.library.ucf.edu/scopus2015/1765