PhD Scientific Days 2021

NE_I_P: Neurosciences I. Posters

ELVISort: encoding latent variables for instant sorting, an artificial intelligence-based end-to-end solution

Text of the abstract

Introduction
The growing number of recording sites of silicon-based probes means that an increasing amount of neural cell activities can be recorded simultaneously, facilitating the investigation of underlying complex neural dynamics. In order to overcome the challenges generated by the increasing number of channels, highly automated signal processing tools are needed.
Aims
Our goal was to build a spike sorting model that can perform as well as offline solutions while maintaining high efficiency, enabling high-performance online sorting.
Method
In this paper we present ELVISort, a deep learning method that combines the detection and clustering of different action potentials in an end-to-end fashion.
Results
The performance of ELVISort is comparable with other spike sorting methods that use manual or semi-manual techniques, while exceeding the methods which use an automatic approach: ELVISort has been tested on three independent datasets and yielded average F1 scores of 0.96, 0.82 and 0.81, which comparable with the results of state-of-the-art algorithms on the same data. We show that despite the good performance, ELVISort is capable to process data in real-time: the time it needs to execute the necessary computations for a sample of given length is only 1/15.71 of its actual duration (i.e. the sampling time multiplied by the number of the sampling points).
Conclusion
ELVISort, because of its end-to-end nature, can exploit the massively parallel processing capabilities of GPUs via deep learning frameworks by processing multiple batches in parallel, with the potential to be used on other cutting-edge AI-specific hardware such as TPUs, enabling the development of integrated, portable and real-time spike sorting systems with similar performance to offline sorters.
Funding
This research was partially funded by Semmelweis 250+ grant (EFOP-3.6.3-VEKOP-16-2017-00009), by the Hungarian Brain Research Program (2017_1.2.1-NKP-2017-00002) and by the Hungarian National Research, Development and Innovation Office (TUDFO/51757-1/2019-ITM). G M and R F are thankful to the Hungarian National Research, Development and Innovation Office (FK132823 and PD124175) grants.

University and Doctoral School

Semmelweis University, János Szentágothai Doctoral School of Neurosciences