DocumentCode :
3241395
Title :
Fast algorithm for neural network reconstruction
Author :
Bittner, Sean ; Siheng Chen ; Kovacevic, Jelena
Author_Institution :
Dept. of ECE, Carnegie Mellon Univ., Pittsburgh, PA, USA
fYear :
2015
fDate :
16-19 April 2015
Firstpage :
866
Lastpage :
869
Abstract :
We propose an efficient and accurate way of predicting the connectivity of neural networks in the brain represented by simulated calcium fluorescence data. Classical methods to neural network reconstruction compute a connectivity matrix whose entries are pairwise likelihoods of directed excitatory connections based on time-series signals of each pair of neurons. Our method uses only a fraction of this computation to achieve equal or better performance. The proposed method is based on matrix completion and a local thresholding technique. By computing a subset of the total entries in the connectivity matrix, we use matrix completion to determine the rest of the connection likelihoods, and apply a local threshold to identify which directed connections exist in the underlying network. We validate the proposed method on a simulated calcium fluorescence dataset. The proposed method outperforms the classical one with 20% of the computation.
Keywords :
biomedical optical imaging; brain; fluorescence; image reconstruction; matrix algebra; medical image processing; neural nets; neurophysiology; time series; brain; connectivity matrix; fast algorithm; neural network reconstruction; simulated calcium fluorescence dataset; time-series signals; Biological neural networks; Calcium; Entropy; Imaging; Neurons; Standards; connectivity analysis; machine learning; nerves;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium on
Conference_Location :
New York, NY
Type :
conf
DOI :
10.1109/ISBI.2015.7164008
Filename :
7164008
Link To Document :
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