Spike sorting algorithms and their efficient hardware implementation: a comprehensive survey

Abstract

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 (BMI), 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 (ASICs), Field-Programmable Gate Arrays (FPGAs), 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.

Tracking spatial patterns and nocturnal arousal in an undisturbed natural population of the pulse-type weakly electric fishGymnotus omarorum

“…Interestingly, we were able to discriminate between residents and visitors and verify that there were many more visitors than residents. Second, it is clear that more fish and more fish movements were detected during nighttime, which was of course expected for a typical nocturnal weakly electric fish 35 , but has seldom been shown in natural unrestrained populations 14,30 . Third, more fish (residents and visitors) and more fish movements were detected in core sampling sites than in edge ones, which confirms that G. omarorum prefers to inhabit the central part of the littoral zone as previously speculated based on daytime manual electrical census 22 .…”

A Comprehensive Exploration of Unsupervised Classification in Spike Sorting: A Case Study on Macaque Monkey and Human Pancreatic Signals

“…Several notable and contemporary contributions within the realm of spike sorting have been highlighted [24]. Nevertheless, providing an exhaustive review of all existing literature in this domain is an intricate task, further compounded by the challenges of conducting a direct comparison across this large number of works.…”

Converting Data for Spiking Neural Network Training

“…In contrast to conventional neural networks that are trained using readily and widely available datasets such as MNIST or ImageNet (which are widely used in computer vision and pattern recognition fields among many others), SNNs require special data representations that capture the temporal dynamics of neural processing in a form of spikes. The precise timing of the spikes becomes crucial for encoding and processing information, necessitating a departure from traditional data formats [3]. Consequently, substantial research efforts are currently focused on developing comprehensive databases tailored explicitly for SNN training and evaluation.…”