Signal Correlation Prediction Using Convolutional Neural Networks

Lukasz Romaszko
; Proceedings of the Neural Connectomics Workshop at ECML 2014, PMLR 46:45-56, 2015.

Abstract

This paper focuses on analysing multiple time series relationships such as correlations between them. We develop a solution for the Connectiomics contest dataset of fluorescence imaging of neural activity recordings — the aim is reconstruction of the wiring between brain neurons. The model is implemented to achieve high evaluation score. Our model took the fourth place in this contest. The performance is similar to the other leading solutions, thus we showed that deep learning methods for time series processing are comparable to the other approaches and have wide opportunities for further improvement. We discuss a range of methods and code optimisations applied for the convolutional neural network for the time series domain.

Cite this Paper


BibTeX
@InProceedings{pmlr-v46-romaszko15, title = {Signal Correlation Prediction Using Convolutional Neural Networks}, author = {Lukasz Romaszko}, booktitle = {Proceedings of the Neural Connectomics Workshop at ECML 2014}, pages = {45--56}, year = {2015}, editor = {Demian Battaglia and Isabelle Guyon and Vincent Lemaire and Jordi Soriano}, volume = {46}, series = {Proceedings of Machine Learning Research}, month = {15 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v46/romaszko15.pdf}, url = {http://proceedings.mlr.press/v46/romaszko15.html}, abstract = {This paper focuses on analysing multiple time series relationships such as correlations between them. We develop a solution for the Connectiomics contest dataset of fluorescence imaging of neural activity recordings — the aim is reconstruction of the wiring between brain neurons. The model is implemented to achieve high evaluation score. Our model took the fourth place in this contest. The performance is similar to the other leading solutions, thus we showed that deep learning methods for time series processing are comparable to the other approaches and have wide opportunities for further improvement. We discuss a range of methods and code optimisations applied for the convolutional neural network for the time series domain.} }
Endnote
%0 Conference Paper %T Signal Correlation Prediction Using Convolutional Neural Networks %A Lukasz Romaszko %B Proceedings of the Neural Connectomics Workshop at ECML 2014 %C Proceedings of Machine Learning Research %D 2015 %E Demian Battaglia %E Isabelle Guyon %E Vincent Lemaire %E Jordi Soriano %F pmlr-v46-romaszko15 %I PMLR %J Proceedings of Machine Learning Research %P 45--56 %U http://proceedings.mlr.press %V 46 %W PMLR %X This paper focuses on analysing multiple time series relationships such as correlations between them. We develop a solution for the Connectiomics contest dataset of fluorescence imaging of neural activity recordings — the aim is reconstruction of the wiring between brain neurons. The model is implemented to achieve high evaluation score. Our model took the fourth place in this contest. The performance is similar to the other leading solutions, thus we showed that deep learning methods for time series processing are comparable to the other approaches and have wide opportunities for further improvement. We discuss a range of methods and code optimisations applied for the convolutional neural network for the time series domain.
RIS
TY - CPAPER TI - Signal Correlation Prediction Using Convolutional Neural Networks AU - Lukasz Romaszko BT - Proceedings of the Neural Connectomics Workshop at ECML 2014 PY - 2015/10/21 DA - 2015/10/21 ED - Demian Battaglia ED - Isabelle Guyon ED - Vincent Lemaire ED - Jordi Soriano ID - pmlr-v46-romaszko15 PB - PMLR SP - 45 DP - PMLR EP - 56 L1 - http://proceedings.mlr.press/v46/romaszko15.pdf UR - http://proceedings.mlr.press/v46/romaszko15.html AB - This paper focuses on analysing multiple time series relationships such as correlations between them. We develop a solution for the Connectiomics contest dataset of fluorescence imaging of neural activity recordings — the aim is reconstruction of the wiring between brain neurons. The model is implemented to achieve high evaluation score. Our model took the fourth place in this contest. The performance is similar to the other leading solutions, thus we showed that deep learning methods for time series processing are comparable to the other approaches and have wide opportunities for further improvement. We discuss a range of methods and code optimisations applied for the convolutional neural network for the time series domain. ER -
APA
Romaszko, L.. (2015). Signal Correlation Prediction Using Convolutional Neural Networks. Proceedings of the Neural Connectomics Workshop at ECML 2014, in PMLR 46:45-56

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