Spike Sorting Algorithms for Large-Scale Neural Recording
Spike sorting refers to the detection,clustering,and identification of distinct extracellular neuronal signals from different recording sites with the aim of reliably assigning them to different putative neurons.This crucial preprocessing is fundamental for neural decoding in neuroscience and represents a prominent research direction in high-bandwidth Brain-Computer Interface(BCI)studies.Conventional spike sorting algorithms involve various steps such as spike detection,spike alignment,feature extraction,and feature clustering.Currently,explosive growth in the number and density of neural electrodes presents significant challenges in terms of the efficiency and accuracy of spike sorting.To address issues such as limited feature extraction capabilities,low Signal-to-Noise Ratio(SNR),and signal superposition,algorithmic advancements,such as artificial intelligence approaches and big data spike sorting solutions have emerged as strategies for enhancing the comprehension of the intricate principles and functions underlying brain activity.This paper provides an overview linking invasive BCIs with neural encoding/decoding and spike sorting methods.Subsequently,it outlines the principles underlying various spike sorting algorithms,while discussing how brain neural signals map onto specific behaviors.Finally,the paper concludes by anticipating future challenges and trends in the development of high-bandwidth neural encoding and decoding.
spike sortinginvasive Brain-Computer Interface(BCI)neural encoding and decodingmachine learningdeep learning
万方数据
