Summary of Functional Classification Of Spiking Signal Data Using Artificial Intelligence Techniques: a Review, by Danial Sharifrazi et al.
Functional Classification of Spiking Signal Data Using Artificial Intelligence Techniques: A Review
by Danial Sharifrazi, Nouman Javed, Javad Hassannataj Joloudari, Roohallah Alizadehsani, Prasad N. Paradkar, Ru-San Tan, U. Rajendra Acharya, Asim Bhatti
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This review aims to provide a comprehensive understanding of the importance and use of Artificial Intelligence (AI) in spike classification for neural activity signals. The task is divided into three main components: preprocessing, classification, and evaluation. Existing methods are introduced and their importance is determined. The primary goal is to provide a perspective on spike classification for future research. The review organizes materials in the spike classification field for future studies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Spike classification is an essential concept in neuroscience that helps clinicians identify vital biomarkers or physical issues such as electrode movements. Traditionally, researchers classified spikes manually, which was not precise enough due to extensive analysis. To address this challenge, AI was introduced into neuroscience to assist clinicians in classifying spikes correctly. This review discusses the importance and use of AI in spike classification, focusing on the recognition of neural activity noises. |
Keywords
* Artificial intelligence * Classification
