Abstract
Connectomics is becoming an increasingly popular area of research. With the recent advances in optical imaging of the neural activity tens of thousands of neurons can be monitored simultaneously. In this paper we present a method of incorporating topological knowledge inside data representation for Random Forest classifier in order to reconstruct the neural connections from patterns of their activities. Proposed technique leads to the model competitive with state-of-the art methods like Deep Convolutional Neural Networks and Graph Decomposition techniques. This claim is supported by the results (5th place with 0.003 in terms of AUC ROC loss to the top contestant) obtained in the connectomics competition organized on the Kaggle platform.
Editors: Demian Battaglia, Isabelle Guyon, Vincent Lemaire, Javier Orlandi, Bisakha Ray, Jordi Soriano
The original form of this article appears in JMLR W&CP Volume 46.
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Notes
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Except normalized difference which is also applied for all non-symmetrical topological and non-topological features.
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Czarnecki, W.M., Jozefowicz, R. (2017). Neural Connectivity Reconstruction from Calcium Imaging Signal Using Random Forest with Topological Features. In: Battaglia, D., Guyon, I., Lemaire, V., Orlandi, J., Ray, B., Soriano, J. (eds) Neural Connectomics Challenge. The Springer Series on Challenges in Machine Learning. Springer, Cham. https://doi.org/10.1007/978-3-319-53070-3_6
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DOI: https://doi.org/10.1007/978-3-319-53070-3_6
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