Recent progress in multi-electrode spike sorting methods

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.

The scale of in vivo electrophysiology has expanded in recent years, with simultaneous recordings across thousands of electrodes now becoming routine. These advances have enabled a wide range of discoveries, but they also impose substantial computational demands. Spike sorting, the procedure that extracts spikes from extracellular voltage measurements, remains a major bottleneck: a dataset collected in a few hours can take days to spike sort on a single machine, and the field lacks rigorous validation of the many spike sorting algorithms and preprocessing steps that are in use. Advancing the speed and accuracy of spike sorting is essential to fully realize the potential of large-scale electrophysiology. Here, we present an end-to-end spike sorting pipeline that leverages parallelization to scale to large datasets. The same workflow can run reproducibly on individual workstations, high-performance computing clusters, or cloud environments, with computing resources tailored to each processing step to reduce costs and execution times. In addition, we introduce a benchmarking pipeline, also optimized for parallel processing, that enables systematic comparison of multiple sorting pipelines. Using this framework, we show that Kilosort4, a widely used spike sorting algorithm, outperforms Kilosort2.5 (). We also show that 7× lossy compression, which substantially reduces the cost of data storage, has minimal impact on spike sorting performance. Together, these pipelines address the urgent need for scalable and transparent spike sorting of electrophysiology data, preparing the field for the coming flood of multi-thousand-channel experiments.

Overlapping is one of the serious problems in the spike sorting of multi-channel recordings. Although techniques to resolve this problem have been developed, there still remains much room for improvement. We present in this paper a new overlapping resolution method which is simple and can easily be incorporated in the existing spike sorting systems. The proposed method combines spike sorting algorithms with the linear projections, such that each projection eliminates single-neuronal activity. Simulations show that our method successfully resolves overlapping in real neural data

Event Abstract Back to Event Optimizing Spike-Sorting Yield through Selection of Optimal Recording Sites in High-density Microelectrode Arrays Jan Müller1*, Felix Franke2, Michele Fiscella1, Urs Frey3, Douglas Bakkum1 and Andreas Hierlemann1 1 ETH Zurich, Department of Biosystems Science and Engineering, Switzerland 2 ETH Zürich, Department of Biosystems Science and Engineering, Switzerland 3 Riken, Quantitative Biology Center, Japan Motivation Recently, various high-density microelectrode arrays (HDMEA) were developed for in-vitro and in-vivo applications. Some of these arrays feature hundreds to thousands of electrodes, from which a subset can be selected for recording at one time [1-4]. Our goal here is to simultaneously extract the activity of a maximum number of identifiable neurons from such recordings. Careful choice of recording electrodes is required, when the number of recorded neurons exceeds the number of available recording channels. When analyzing data from networks of neurons, one is typically interested in the exact relative timing of the occurrence of action potentials in different neurons. Here, we consider the assignment of recorded action potentials to individual neurons a classification problem. Therefore, we propose a metric relying on linear discriminant analysis (LDA) to compute the expected classification error for a given electrode arrangement. Moreover, we propose an algorithm to automatically find optimal electrode configurations. Material and Methods HDMEA: To demonstrate the feasibility of our method, we used a recently developed HDMEA featuring 26,400 Pt microelectrodes, arranged in a grid-like configuration with a center-to-center pitch of 17.5 μm [3]. 1024 of these electrodes can be selected for simultaneous recording by means of low-noise amplifiers (2.4 μV rms in the action potential signal band 300 Hz–10 kHz). Algorithm: The extent to which action potentials from two neurons can be clearly distinguished depends on their multi-electrode waveforms. The best separation can be achieved if information from all electrodes is available. However, only a subset of all available electrodes can be used for recording at the same time. Our method starts out by successively recording through blocks of all 26,400 electrodes, spike sorting each of the recordings, and building templates for each neuron (Step 1). Using the templates from all neurons and linear discriminant analysis [5], we compute the expected spike sorting error (ESSE) using all electrodes. We then compute for each electrode the ESSE under the assumption that the corresponding electrode would not be used for recording (Step 2). Electrodes, the removal of which increases the ESSE by the least amount will then be discarded (Step 3). Step 2 and 3 are iterated until the desired number of electrodes, i.e., the number of available readout channels is reached. Results We recorded from a patch of hamster retina for ten minutes per electrode. Based on this data, action potential templates were computed, and the algorithm has been applied to identify the optimal recording electrodes. The classification error decays exponentially upon increasing the number of used electrodes. Discussion/Conclusion We have presented a framework to assess the quality of a particular recording scenario. The framework quantifies the error probability for discriminating between all a-priori known templates for all present neurons. The algorithm was applied to recordings from a retina performed with a recently developed HDMEA. Our approach is related to earlier work [6]. However, by relying on linear discriminant analysis, we make - in a Bayesian sense - optimal use of the available recording resources. [1] Lopez, Carolina M., et al. "A 966-electrode neural probe with 384 configurable channels in 0.13μm SOI CMOS", ISSCC (2016), 392-393. [2] Viswam, Vijay, et al. "Multi-Functional Microelectrode Array System featuring 59’760 Electrodes, 2048 Electrophysiology Channels, Impedance and Neurotransmitter Measurement Units", ISSCC (2016), 394-396. [3] Ballini, Marco, et al. "A 1024-Channel CMOS microelectrode array with 26,400 electrodes for recording and stimulation of electrogenic cells in vitro." Solid-State Circuits, IEEE Journal of 49.11 (2014): 2705-2719. [4] Seidl, Karsten, et al. "CMOS-based high-density silicon microprobe arrays for electronic depth control in intracortical neural recording" Microelectromechanical Systems, Journal of, 20(6):1439– 1448, 2011. [5] Franke, Felix, et al. "Bayes Optimal Template Matching for Spike Sorting – Combining Fisher Discriminant Analysis with Optimal Filtering, " J. Comput. Neurosci., vol. 38, no. 3, pp. 439–459, 2015. [6] Vysotska, Olga, "Automatic channel selection and neural signal estimation across channels of neural probes", International Conference on Intelligent Robots and Systems (2014), 1453–1459. Keywords: Retina, CMOS, Spike-sorting, high-density microelectrode array (HDMEA) Conference: MEA Meeting 2016 | 10th International Meeting on Substrate-Integrated Electrode Arrays, Reutlingen, Germany, 28 Jun - 1 Jul, 2016. Presentation Type: Poster Presentation Topic: MEA Meeting 2016 Citation: Müller J, Franke F, Fiscella M, Frey U, Bakkum D and Hierlemann A (2016). Optimizing Spike-Sorting Yield through Selection of Optimal Recording Sites in High-density Microelectrode Arrays. Front. Neurosci. Conference Abstract: MEA Meeting 2016 | 10th International Meeting on Substrate-Integrated Electrode Arrays. doi: 10.3389/conf.fnins.2016.93.00122 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 22 Jun 2016; Published Online: 24 Jun 2016. * Correspondence: Dr. Jan Müller, ETH Zurich, Department of Biosystems Science and Engineering, Basel, Switzerland, jan.mueller@bsse.ethz.ch Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Jan Müller Felix Franke Michele Fiscella Urs Frey Douglas Bakkum Andreas Hierlemann Google Jan Müller Felix Franke Michele Fiscella Urs Frey Douglas Bakkum Andreas Hierlemann Google Scholar Jan Müller Felix Franke Michele Fiscella Urs Frey Douglas Bakkum Andreas Hierlemann PubMed Jan Müller Felix Franke Michele Fiscella Urs Frey Douglas Bakkum Andreas Hierlemann Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Unsupervised spike sorting, a vital processing step in real-time brain-implantable microsystems, is faced with the prominent challenge of managing nonstationarity in neural signals. In long-term recordings, spike waveforms gradually change and new source neurons are likely to become activated. Adaptive spike sorters combined with on-implant training units effectively process the nonstationary signals at the cost of high hardware resource utilization. On the other hand, static approaches, while being hardware-friendly, are subjected to decreased processing performance in such recordings where the neural signal characteristics gradually change. To strike a balance between the hardware cost and processing performance, this study proposes a hardware-efficient novelty-aware spike sorting approach that is capable of dealing with both variated spike waveforms and spike waveforms generated from new source neurons. Its improved hardware efficiency compared to adaptive ones and capability of dealing with nonstationary signals make it attractive for implantable applications. The proposed novelty-aware spike sorting especially would be a good fit for brain-computer interfaces where long-term, real-time interaction with the brain is required, and the available on-implant hardware resources are limited. Our unsupervised spike sorting benefits from a novelty detection process to deal with neural signal variations. It tracks the spike features so that in case of detecting an unexpected change (novelty detection) both on and off-implant parameters are updated to preserve the performance in new state. To make the proposed approach agile enough to be suitable for brain implants, the on-implant computations are reduced while the computational burden is realized off-implant. The performance of our proposed approach is evaluated using both synthetic and real datasets. The results demonstrate that, in the mean, it is capable of detecting 94.31% of novel spikes (wave-drifted or emerged spikes) with a classification accuracy (CA) of 96.31%. Moreover, an FPGA prototype of the on-implant circuit is implemented and tested. It is shown that in comparison to the OSORT algorithm, a pivotal spike sorting method, our spike sorting provides a higher CA at significantly lower hardware resources. The proposed circuit is also implemented in a 180-nm standard CMOS process, achieving a power consumption of 1.78[Formula: see text][Formula: see text] per channel and a chip area of 0.07[Formula: see text]mm2 per channel.

Spike sorting is a fundamental process for decoding neural activity, involving preprocessing, spike detection, feature extraction, clustering, and validation. However, conventional spike sorting methods are highly fragmented, labor-intensive, and heavily reliant on expert manual curation, limiting their scalability and reproducibility. This challenge has become more pressing with advances in neural recording technology, such as high-density Neuropixels for large-scale neural recording or flexible electrodes for long-term stable recording over months to years. The volume and complexity of these datasets make manual curation infeasible, requiring an automated and scalable solution. Here, we introduce SpikeAgent, a multimodal large language model (LLM)-based AI agent that automates and standardizes the entire spike sorting pipeline. Unlike traditional approaches, SpikeAgent integrates multiple LLM backends, coding functions, and established algorithms, autonomously performing spike sorting with reasoning-based decision-making and real-time interaction with intermediate results. It generates interpretable reports, providing transparent justifications for each sorting decision, enhancing transparency and reliability. We benchmarked SpikeAgent against human experts across various neural recording technology, demonstrating its versatility and ability to achieve curation consistency that are equal to, or even higher than human experts. It also drastically reduces the expertise barrier and accelerates the curation and validation time by orders of magnitude. Moreover, it enables automated interpretability of the neural spiking data, which cannot be achieved by any conventional methods. SpikeAgent presents a paradigm shift in processing signals for neuroscience and brain-computer interfaces, while laying the ground for AI agent-augmented science across various domains.

We propose an unsupervised compressed sensing (CS)-based framework to compress, recover, and cluster neural action potentials. This framework can be easily integrated into high-density multi-electrode neural recording VLSI systems. Embedding spectral clustering and group structures in dictionary learning, we extend the proposed framework to unsupervised spike sorting without prior label information. Additionally, we incorporate group sparsity concepts in the dictionary learning to enable the framework for multi-channel neural recordings, as in tetrodes. To further improve spike sorting success rates in the CS framework, we embed template matching in sparse coding to jointly predict clusters of spikes. Our experimental results demonstrate that the proposed CS-based framework can achieve a high compression ratio (8:1 to 20:1), with a high quality reconstruction performance (>8 dB) and a high spike sorting accuracy (>90%).

BackgroundPrincipal component analysis (PCA) has been widely employed for automatic neuronal spike sorting. Calculating principal components (PCs) is computationally expensive, and requires complex numerical operations and large memory resources. Substantial hardware resources are therefore needed for hardware implementations of PCA. General Hebbian algorithm (GHA) has been proposed for calculating PCs of neuronal spikes in our previous work, which eliminates the needs of computationally expensive covariance analysis and eigenvalue decomposition in conventional PCA algorithms. However, large memory resources are still inherently required for storing a large volume of aligned spikes for training PCs. The large size memory will consume large hardware resources and contribute significant power dissipation, which make GHA difficult to be implemented in portable or implantable multi-channel recording micro-systems.MethodIn this paper, we present a new algorithm for PCA-based spike sorting based on GHA, namely stream-based Hebbian eigenfilter, which eliminates the inherent memory requirements of GHA while keeping the accuracy of spike sorting by utilizing the pseudo-stationarity of neuronal spikes. Because of the reduction of large hardware storage requirements, the proposed algorithm can lead to ultra-low hardware resources and power consumption of hardware implementations, which is critical for the future multi-channel micro-systems. Both clinical and synthetic neural recording data sets were employed for evaluating the accuracy of the stream-based Hebbian eigenfilter. The performance of spike sorting using stream-based eigenfilter and the computational complexity of the eigenfilter were rigorously evaluated and compared with conventional PCA algorithms. Field programmable logic arrays (FPGAs) were employed to implement the proposed algorithm, evaluate the hardware implementations and demonstrate the reduction in both power consumption and hardware memories achieved by the streaming computingResults and discussionResults demonstrate that the stream-based eigenfilter can achieve the same accuracy and is 10 times more computationally efficient when compared with conventional PCA algorithms. Hardware evaluations show that 90.3% logic resources, 95.1% power consumption and 86.8% computing latency can be reduced by the stream-based eigenfilter when compared with PCA hardware. By utilizing the streaming method, 92% memory resources and 67% power consumption can be saved when compared with the direct implementation of GHA.ConclusionStream-based Hebbian eigenfilter presents a novel approach to enable real-time spike sorting with reduced computational complexity and hardware costs. This new design can be further utilized for multi-channel neuro-physiological experiments or chronic implants.

A large amount of neurophysiological research is based on the study of biological neural populations. The data is gathered from extra-cellular recordings with multi-electrode arrays (MEAs). The signal is a stream containing an unknown number of neural sources superpositioned with non-stationary noise components. In order to analyze the recorded spike trains, a prior separation into its individual components is required. The increasing number of sensors on a MEA surface demand an automatic spike sorting procedure. In this article the proposed spike sorting method replaces the manual steps with artificial neural networks to enable an unsupervised separation of mixed signal components. Furthermore the artificial neural network based feature extraction allows realtime processing and ensures high flexbibility and adaptivity for unknown signals with non-stationary noise components.

Spike sorting is one of the cornerstones of extracellular electrophysiology. By leveraging advanced signal processing and data analysis techniques, spike sorting makes it possible to detect, isolate, and map single neuron spiking activity from both in vivo and in vitro extracellular electrophysiological recordings. A crucial step of any spike sorting pipeline is to reduce the dimensionality of the recorded spike waveform data. Reducing the dimensionality of the processed data is a near-universal practice, fundamentally motivated by the use of clustering algorithms responsible to detect, isolate, and sort the recorded putative neurons. In this paper, we propose and illustrate on both synthetic and experimental data that employing the nonlinear dimensionality reduction technique Uniform Manifold Approximation and Projection (UMAP) can drastically improve the performance, efficiency, robustness, and scalability of spike sorting pipelines without increasing their computational cost. We show how replacing the linear or ad hoc, expert-defined, supervised nonlinear dimensionality reduction methods commonly used in spike sorting pipelines by the unsupervised, mathematically grounded, nonlinear dimensionality reduction method provided by UMAP drastically increases the number of correctly sorted neurons, makes the identification of quieter, seldom spiking neurons more reliable, enables deeper and more precise explorations and analysis of the neural code, and paves new ways toward more efficient and end-to-end automatable spike sorting pipelines of large-scale extracellular neural recording as those produced by high-density multielectrode arrays.

Spike sorting is the process of separating the extracellular recording of the brain signal into one unit activity. There are a number of proposed algorithms for this purpose, but there is still no acceptable solution. In this paper a spike sorting method has been proposed based on the Euclidean distance of the most effective features of spikes represented by principle components (PCs) of the detected and aligned spikes. The assessments of the method, based on signal-to-noise ratio (SNR) representing background noise, showed that the method performed spike sorting to a high level of accuracy.

Neural decoding and its applications to brain computer interfaces (BCI) are essential for understanding the association between neural activity and behavior. A prerequisite for many decoding approaches is spike sorting, the assignment of action potentials (spikes) to individual neurons. Current spike sorting algorithms, however, can be inaccurate and do not properly model uncertainty of spike assignments, therefore discarding information that could potentially improve decoding performance. Recent advances in high-density probes (e.g., Neuropixels) and computational methods now allow for extracting a rich set of spike features from unsorted data; these features can in turn be used to directly decode behavioral correlates. To this end, we propose a spike sorting-free decoding method that directly models the distribution of extracted spike features using a mixture of Gaussians (MoG) encoding the uncertainty of spike assignments, without aiming to solve the spike clustering problem explicitly. We allow the mixing proportion of the MoG to change over time in response to the behavior and develop variational inference methods to fit the resulting model and to perform decoding. We benchmark our method with an extensive suite of recordings from different animals and probe geometries, demonstrating that our proposed decoder can consistently outperform current methods based on thresholding (i.e. multi-unit activity) and spike sorting. Open source code is available at https://github.com/yzhang511/density_decoding.

Much development has been directed toward improving the performance and automation of spike sorting. This continuous development, while essential, has contributed to an over-saturation of new, incompatible tools that hinders rigorous benchmarking and complicates reproducible analysis. To address these limitations, we developed SpikeInterface, a Python framework designed to unify preexisting spike sorting technologies into a single codebase and to facilitate straightforward comparison and adoption of different approaches. With a few lines of code, researchers can reproducibly run, compare, and benchmark most modern spike sorting algorithms; pre-process, post-process, and visualize extracellular datasets; validate, curate, and export sorting outputs; and more. In this paper, we provide an overview of SpikeInterface and, with applications to real and simulated datasets, demonstrate how it can be utilized to reduce the burden of manual curation and to more comprehensively benchmark automated spike sorters.

Transmitting large amounts of data sensed by multi-electrode array devices is widely considered to be a challenging problem in designing implantable neural recording systems. Spike sorting is an important step to reducing the data bandwidth before wireless data transmission. The feasibility of spike sorting algorithms in scaled CMOS technologies, which typically operate on low frequency neural spikes, is determined largely by its power consumption, a dominant portion of which is leakage power. Our goal is to explore energy saving architectures for online spike sorting without sacrificing performance. In the face of non-stationary neural data, training is not always a feasible option. We present a low-power architecture for real-time online spike sorting that does not require any training period and has the capability to quickly respond to the changing spike shapes.

Spike sorting is an important processing step in various neuroscientific and clinical studies. Energy-efficient spike-sorting ASICs are necessary to allow real-time processing of multi-channel, wireless neural recordings. Spike-sorting ASICs have to meet stringent power-density constraints and must provide significant data-rate reduction for wireless transmission. Most existing designs either provide only spike detection for multi-channel processing, or they provide detection and feature extraction only for a single channel. In this paper, we demonstrate the design of a spike-sorting DSP chip that can perform detection, alignment, and feature extraction simultaneously for 64 channels. Spike-sorting algorithms chosen based on a complexity-performance analysis were implemented on ASIC using a MATLAB/Simulink-based architecture design framework. Energy-delay tradeoffs of the design were analyzed to identify the optimal degree of interleaving. The chip was implemented with a modular architecture, and can be configured to process 16, 32, 48, or 64 channels. Inactive cores are power-gated when the chip is operated to process a reduced number of channels. The chip, implemented in a 90-nm CMOS process, has a power dissipation of 130 μW (power density of 30 μW/mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) when processing all 64 channels and provides a data-rate reduction of 91.25% (11.71 Mb/s to 1.02 Mb/s).