PhD Scientific Days 2020

Neurosciences I. Posters

Two Phase Spike Detection Using Deep Learning

Előadó neve

Dr. Rokai, János

Előadó munkahelye

Károly Rácz School of PhD Studies, Semmelweis University, Budapest, Hungary

Előadó telefonszáma

+36 30 710 1719

Előadó e-mail címe

rokaijano@gmail.com

Az előadás címe

Two Phase Spike Detection Using Deep Learning

Szerző(k) neve és munkahelye

János Rokai1,3, Richárd Fiáth2,3, István Ulbert2,3, Gergely Márton2,3

1 Károly Rácz School of PhD Studies, Semmelweis University, Budapest, Hungary
2 Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural
Sciences, H-1117 Budapest, Hungary
3 Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, H-
1083 Budapest, Hungary

Szekció

Neurosciences I. Posters

Language of the presentation

English

Section, first choice

Neurosciences

Section, second choice

Neurosciences

Összefoglaló szövege

Automatic identification of the single unit activities is a major part in the analysis of the electrophysiological data recorded from within the central nervous system. Despite multiple unsupervised methods were proposed to detect and sort neural activity, they require hyperparameter tuning for every individual recording.

The goal was to build a robust, reliable detector which is unaffected by any parameters of the recording site. Our proposal is a new detection system, which utilizes deep learning tools to induce generalization.

The proposed detection system consists of a pre-detector and a main detector. The electrophysiological data is filtered with the pre-detector, ensuring that the information arriving to the main detector has a higher probability being a positive sample. The pre-detector system is built with low computational cost and high operating frequency in mind, while the main detector with moderate computational cost and operating frequency, keeping the option of a future real-time detector open.
To evaluate the performance of our model we used 5 different recordings and cross-validated them having at every step training and validation samples from 4 datasets, while the remaining one serving as the test dataset.

For evaluating the performance we used the recall, precision and accuracy metrics. We cross-validated our model on small epoch size and selected the best performing configuration which was trained for a longer time, with the following results: 89.59%, 61,40% recall, 88,77%, 50,32% precision and 95%, 81% accuracy for validation and test datasets respectively.

Additional Information

Supervisor: Gergely Márton
E-mail address: marton.gergely@ttk.mta.hu

Bemutatás módja

Poszter

Kind

Szabad

Status

elfogadva

Accepted presentation method

poszter

Előadás fájl jóváhagyás

nem rendelkezett róla

Előadó

4634

Start

12:47

End

12:50

Authors (legacy)

János Rokai1,3, Richárd Fiáth2,3, István Ulbert2,3, Gergely Márton2,3

1 Károly Rácz School of PhD Studies, Semmelweis University, Budapest, Hungary
2 Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural
Sciences, H-1117 Budapest, Hungary
3 Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, H-
1083 Budapest, Hungary