.png){kind=icon}
返回
Spike sorting algorithms and their efficient hardware implementation: a comprehensive survey
.png){kind=icon}
PRE
AI
DOI：10.1088/1741-2552/acc7cc.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Objective. Spike sorting is a set of techniques used to analyze extracellular neural recordings, attributing individual spikes to individual neurons. This field has gained significant interest in neuroscience due to advances in implantable microelectrode arrays, capable of recording thousands of neurons simultaneously. High-density electrodes, combined with efficient and accurate spike sorting systems, are essential for various applications, including brain machine interfaces (BMIs), experimental neural prosthetics, real-time neurological disorder monitoring, and neuroscience research. However, given the resource constraints of modern applications, relying solely on algorithmic innovation is not enough. Instead, a co-optimization approach that combines hardware and spike sorting algorithms must be taken to develop neural recording systems suitable for resource-constrained environments, such as wearable devices and BMIs. This co-design requires careful consideration when selecting appropriate spike-sorting algorithms that match specific hardware and use cases. Approach. We investigated the recent literature on spike sorting, both in terms of hardware advancements and algorithms innovations. Moreover, we dedicated special attention to identifying suitable algorithm-hardware combinations, and their respective real-world applicabilities. Main results. In this review, we first examined the current progress in algorithms, and described the recent departure from the conventional '3-step' algorithms in favor of more advanced template matching or machine-learning-based techniques. Next, we explored innovative hardware options, including application-specific integrated circuits, field-programmable gate arrays, and in-memory computing devices (IMCs). Additionally, the challenges and future opportunities for spike sorting are discussed. Significance. This comprehensive review systematically summarizes the latest spike sorting techniques and demonstrates how they enable researchers to overcome traditional obstacles and unlock novel applications. Our goal is for this work to serve as a roadmap for future researchers seeking to identify the most appropriate spike sorting implementations for various experimental settings. By doing so, we aim to facilitate the advancement of this exciting field and promote the development of innovative solutions that drive progress in neural engineering research.
Keyword:
spike sorting
hardware
machine learning
neuromorphic engineering
期刊
IF:
3.8
论文数:
4.0K
被引数:
1.4W
机构
引用论文
Lightweight optimization of the front end structure of an automobile body using entropy-based grey relational analysis.png)
Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering
Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile EngineeringIF0
PREAI
分享收藏A framework for on-implant spike sorting based on salient feature selectionNATURE COMMUNICATIONS
OAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical ApplicationsOAAI
OAAI分享收藏Multichannel parallel processing of neural signals in memristor arrays忆阻器阵列神经信号的多通道并行处理SCIENCE ADVANCESOAAI
SCIENCE ADVANCESIF12.5
OAAI分享收藏Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting基于OxRAM突触的尖峰神经网络用于实时无监督尖峰排序OAAI
OAAI分享收藏Comparative Study of Online Open Circuit Voltage Estimation Techniques for State of Charge Estimation of Lithium-Ion BatteriesBatteriesOAAI
BatteriesIF0
OAAI分享收藏
查看更多内容