Artificial Neural Networks in Action for an Automated Cell-Type Classification of Biological Neural Networks
Eirini Troullinou,Grigorios Tsagkatakis,Spyridon Chavlis,Gergely F. Turi,Wen-Ke Li,Attila Losonczy,Panagiotis Tsakalides,Panayiota Poirazi +7 more
- Vol. 5, Iss: 5, pp 755-767
TLDR
This work examines three different deep learning models aiming at an automated neuronal cell-type classification and compare their performance, and demonstrates the efficacy and potent capabilities for each one of the proposed schemes.Abstract:
Identification of different neuronal cell types is critical for understanding their contribution to brain functions. Yet, automated and reliable classification of neurons remains a challenge, primarily because of their biological complexity. Typical approaches include laborious and expensive immunohistochemical analysis while feature extraction algorithms based on cellular characteristics have recently been proposed. The former rely on molecular markers, which are often expressed in many cell types, while the latter suffer from similar issues: finding features that are distinctive for each class has proven to be equally challenging. Moreover, both approaches are time consuming and demand a lot of human intervention. In this work we establish the first, automated cell-type classification method that relies on neuronal activity rather than molecular or cellular features. We test our method on a real-world dataset comprising of raw calcium activity signals for four neuronal types. We compare the performance of three different deep learning models and demonstrate that our method can achieve automated classification of neuronal cell types with unprecedented accuracy.read more
Citations
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A Generative Neighborhood-based Deep Autoencoder for Robust Imbalanced Classification
Eirini Troullinou,Gregory Tsagkatakis,Attila Losonczy,Panayiota Poirazi,Panagiotis Tsakalides +4 more
TL;DR: GenDA as discussed by the authors is a generative neighborhood-based deep autoencoder, which can be successfully applied to both image and time series data, and can be used to learn latent representations based on the neighboring embedding space of the samples.
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