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

Socpus ID

84954104243 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84954104243

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