Loading...

Unsupervised Neuronal Spike Sorting by Deep Learning Methods

Rahmani, Saeed | 2020

324 Viewed
  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 54241 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Sharifi Zarchi, Ali
  7. Abstract:
  8. Unsupervised neural spike sorting is a crucial tool in studying neural systems in the resolution of a neural cell. In extracellular recording from neural cells, the voltage of media is captured by the electrodes. The situation is possible that an electrode record activity of multiple neurons at the same time. The spike sorting goal is assigning each spike (extracellular recorded neural action potential) to a neural cell that generates it. Conventionally, more than one electrode is used to recording media voltages. The electrodes are placed in a small space as a single device called a multi-electrode array. After the spike sorting procedure, the occurrence time of activity of several cells is available that presents valuable information about the state of neural culture. The information can be used in the vast majority of analyses and manipulations. Conventionally, unsupervised spike sorting is divided into multiple steps that are high pass frequency filtering, spike detection, and feature extraction. Due to recent research, deep learning and self-supervised techniques achieve the state of arts in substantial applications. In this research, a novel procedure based on deep learning and self-supervised learning techniques proposed to find appropriate features for clustering. The proposed method dominate conventional methods likes PCA and DWT. Despite the problem's unsupervised nature, the problem redefines as a supervised problem with an imaginary goal to learn concepts of similarity and distances between spikes. Different research groups using unique tools and procedures for extracellular recording. Thus, the ANNs can be tuned to achieve the best performance based on the new data
  9. Keywords:
  10. Deep Learning ; Neural Networks ; Feature Extraction ; Self-Supervised Learning ; Unsupervised Learning ; Spike Sorting ; Deep Clustering ; Unsupervised Spike Sorting

 Digital Object List

 Bookmark

No TOC