Making Sense of Neural Activity: Advancements in Spike Sorting

Published on June 14, 2022

Understanding neural activity is like untangling a complex web. Detecting and attributing neural activity to specific neurons, known as spike sorting, has become an essential tool in analyzing electrophysiological data. With advancements in recording techniques and algorithms, spike sorting has evolved over the past 5 years. This review explores the innovations in neural sensors, recording procedures, and spike sorting strategies. By sharing concepts, results, and questions, this article aims to bring us closer to decoding neural transcripts. It’s like putting together puzzle pieces to reveal the bigger picture of the brain’s functioning. Want to dive deeper into the world of spike sorting? Check out the full research article!

The meaning behind neural single unit activity has constantly been a challenge, so it will persist in the foreseeable future. As one of the most sourced strategies, detecting neural activity in high-resolution neural sensor recordings and then attributing them to their corresponding source neurons correctly, namely the process of spike sorting, has been prevailing so far. Support from ever-improving recording techniques and sophisticated algorithms for extracting worthwhile information and abundance in clustering procedures turned spike sorting into an indispensable tool in electrophysiological analysis. This review attempts to illustrate that in all stages of spike sorting algorithms, the past 5 years innovations’ brought about concepts, results, and questions worth sharing with even the non-expert user community. By thoroughly inspecting latest innovations in the field of neural sensors, recording procedures, and various spike sorting strategies, a skeletonization of relevant knowledge lays here, with an initiative to get one step closer to the original objective: deciphering and building in the sense of neural transcript.

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