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Spike Sorting

Marc D. Binder, Nobutaka Hirokawa, Uwe Windhorst · 2008

Cited by 0

Spike sorting

Rodrigo Quian Quiroga · Scholarpedia · 2007

Cited by 74Open access

- Spike Sorting

2013

Cited by 4

Spike sorting

Rodrigo Quian Quiroga · Current Biology · 2012

Cited by 105Open access

Spike Sorting

Ge, Di, Dario, Farina · HAL (Le Centre pour la Communication Scientifique Directe) · 2013

Excitable cells, such as neurons, produce action potentials (APs) that in extracel- lular recordings are often referred to as spikes. In these recordings, APs indeed appear as sharp spikes deviated from the noisy baseline. The contributions of each cell must be isolated from the background noise and from those of the other cells. The process of estimating one or more AP waveforms and their discharge instances (firing patterns) from multiunit recordings is known as spike sorting. Resolving overlapped APs is a major difficulty in automatic spike sorting methods. Therefore, this chapter is in large part dedicated to discussing this issue; we focus on state-of-the-art techniques addressing the problem of spike sorting, including the resolution of overlapped APs. In the section "Signal model", we propose the mathematical modeling of multiunit recordings and the complexity in the resolution of overlapped APs. Then, in section "State of the art," we sum- marize state-of-the-art spike sorting algorithms and discuss the advantages and limitations of each and the applicability of these methods for different types of experimental demands. The final section provides the conclusions.

Cited by 0

Benchmarked spike sorting

Nina Vogt · Nature Methods · 2020

Cited by 1Open access

Spike Train Decoding Without Spike Sorting

Valérie Ventura · Neural Computation · 2007

We propose a novel paradigm for spike train decoding, which avoids entirely spike sorting based on waveform measurements. This paradigm directly uses the spike train collected at recording electrodes from thresholding the bandpassed voltage signal. Our approach is a paradigm, not an algorithm, since it can be used with any of the current decoding algorithms, such as population vector or likelihood-based algorithms. Based on analytical results and an extensive simulation study, we show that our paradigm is comparable to, and sometimes more efficient than, the traditional approach based on well-isolated neurons and that it remains efficient even when all electrodes are severely corrupted by noise, a situation that would render spike sorting particularly difficult. Our paradigm will also save time and computational effort, both of which are crucially important for successful operation of real-time brain-machine interfaces. Indeed, in place of the lengthy spike-sorting task of the traditional approach, it involves an exact expectation EM algorithm that is fast enough that it could also be left to run during decoding to capture potential slow changes in the states of the neurons.

Cited by 51

Spike sorting with Kilosort4

Marius Pachitariu, Shashwat Sridhar, Jacob R. Pennington et al. · Nature Methods · 2024

Spike sorting is the computational process of extracting the firing times of single neurons from recordings of local electrical fields. This is an important but hard problem in neuroscience, made complicated by the nonstationarity of the recordings and the dense overlap in electrical fields between nearby neurons. To address the spike-sorting problem, we have been openly developing the Kilosort framework. Here we describe the various algorithmic steps introduced in different versions of Kilosort. We also report the development of Kilosort4, a version with substantially improved performance due to clustering algorithms inspired by graph-based approaches. To test the performance of Kilosort, we developed a realistic simulation framework that uses densely sampled electrical fields from real experiments to generate nonstationary spike waveforms and realistic noise. We found that nearly all versions of Kilosort outperformed other algorithms on a variety of simulated conditions and that Kilosort4 performed best in all cases, correctly identifying even neurons with low amplitudes and small spatial extents in high drift conditions.

Cited by 341Open access

Automated spike sorting curation

Spivak Lidor, Remenick Tali, Stark Eran · Zenodo (CERN European Organization for Nuclear Research) · 2022

Identifying spike timing of individual neurons recorded simultaneously is essential for investigating the neuronal circuits that underlie cognitive functions. Recent developments allow the simultaneous recording of multi-channel extracellular spikes from hundreds of neurons. Yet classifying extracellular spikes into putative source neurons remains a challenge. Here, we set out to develop a fully automated algorithm which can replace manual curation for an existing multi-channel spike sorting solution. As an input to the automated curator (AC), we use KlustaKwik to over-cluster the spikes automatically, yielding numerous spike clusters. From that point onwards, we refer to spike sorting not as a clustering problem, but rather as a sequence of two binary classification problems. To perform automatic curation, we first use a “noise classifier” to remove non-neuronal clusters. Second, we use a “merge classifier” to determine whether any given pair of clusters should be merged. The output of the AC is a set of well-isolated units.

Cited by 0Open access

Spike sorting AI agent

Zuwan Lin, Arnau Marin‐Llobet, Jong‐Min Baek et al. · bioRxiv (Cold Spring Harbor Laboratory) · 2025

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.

Cited by 8Open access

Spike Sorting: The Prehistory

Pouzat, Christophe · Zenodo (CERN European Organization for Nuclear Research) · 2022

A talk given at the CORTICO 2022 workshop, March 16 2022.

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Spike Sorting Algorithms Comparison

Jiří Wild, Tomáš Sieger, Daniel Novák et al. · Frontiers in Neuroinformatics · 2009

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How do spike collisions affect spike sorting performance?

Samuel Garcia, Alessio Paolo Buccino, Pierre Yger · bioRxiv (Cold Spring Harbor Laboratory) · 2021

Abstract Recently, a new generation of devices have been developed to record neural activity simultaneously from hundreds of electrodes with a very high spatial density, both for in vitro and in vivo applications. While these advances enable to record from many more cells, they also dramatically increase the amount overlapping “synchronous” spikes (colliding in space and/or in time), challenging the already complicated process of spike sorting (i.e. extracting isolated single-neuron activity from extracellular signals). In this work, we used synthetic ground-truth recordings to quantitatively benchmark the performance of state-of-the-art spike sorters focusing specifically on spike collisions. Our results show that while modern template-matching based algorithms are more accurate than density-based approaches, all methods, to some extent, failed to detect synchronous spike events of neurons with similar extracellular signals. Interestingly, the performance of the sorters is not largely affected by the the spiking activity in the recordings, with respect to average firing rates and spike-train correlation levels.

Cited by 8Open access

How Do Spike Collisions Affect Spike Sorting Performance?

Samuel Garcia, Alessio P. Bucciono, Pierre Yger · eNeuro · 2022

Recently, a new generation of devices have been developed to record neural activity simultaneously from hundreds of electrodes with a very high spatial density, both for <i>in vitro</i> and <i>in vivo</i> applications. While these advances enable to record from many more cells, they also challenge the already complicated process of spike sorting (i.e., extracting isolated single-neuron activity from extracellular signals). In this work, we used synthetic ground-truth recordings with controlled levels of correlations among neurons to quantitatively benchmark the performance of state-of-the-art spike sorters focusing specifically on spike collisions. Our results show that while modern template-matching-based algorithms are more accurate than density-based approaches, all methods, to some extent, failed to detect synchronous spike events of neurons with similar extracellular signals. Interestingly, the performance of the sorters is not largely affected by the spiking activity in the recordings, with respect to average firing rates and spike-train correlation levels. Since the performances of all modern spike sorting algorithms can be affected as function of the activity of the recorded neurons, scientific claims on correlations and synchrony should be carefully assessed based on the analysis provided in this paper.

Cited by 20Open access

Feature Engineering for Spike Sorting

Arnold Ackerlauer · Digital Repository (National Repository of Grey Literature) · 2023

Spike sorting is the process of identifying the neuron from which a spike originated from and can be used in various applications such as brain-machine interfaces. This thesis is based on simulated, continuous recordings of single neuronal cells. Using several feature engineering techniques we were able to propose a feature set which can be used for spike sorting.

Cited by 0

Technique(s) for Spike - Sorting

Pouzat, Christophe · ArXiv.org · 2004

Spike-sorting techniques attempt to classify a series of noisy electrical waveforms according to the identity of the neurons that generated them. Existing techniques perform this classification ignoring several properties of actual neurons that can ultimately improve classification performance. In this chapter, after illustrating the spike-sorting problem with real data, we propose a more realistic spike train generation model. It incorporates both a description of "non trivial" (ie, non Poisson) neuronal discharge statistics and a description of spike waveform dynamics (eg, the events amplitude decays for short inter-spike intervals). We show that this spike train generation model is analogous to a one-dimensional Potts spin glass model. We can therefore use the computational methods which have been developed in fields where Potts models are extensively used. These methods are based on the construction of a Markov Chain in the space of model parameters and spike train configurations, where a configuration is defined by specifying a neuron of origin for each spike. This Markov Chain is built such that its unique stationary density is the posterior density of model parameters and configurations given the observed data. A Monte Carlo simulation of the Markov Chain is then used to estimate the posterior density. The theoretical background on Markov chains is provided and the way to build the transition matrix of the Markov Chain is illustrated with a simple, but realistic, model for data generation . Simulated data are used to illustrate the performance of the method and to show that it can easily cope with neurons generating spikes with highly dynamic waveforms and/or generating strongly overlapping clusters on Wilson plots.

Cited by 0Open access

Spike Sorting by Stochastic Simulation

Di Ge, Éric Le Carpentier, Jérôme Idier et al. · IEEE Transactions on Neural Systems and Rehabilitation Engineering · 2011

The decomposition of multiunit signals consists of the restoration of spike trains and action potentials in neural or muscular recordings. Because of the complexity of automatic decomposition, semiautomatic procedures are sometimes chosen. The main difficulty in automatic decomposition is the resolution of temporally overlapped potentials. In a previous study , we proposed a Bayesian model coupled with a maximum a posteriori (MAP) estimator for fully automatic decomposition of multiunit recordings and we showed applications to intramuscular EMG signals. In this study, we propose a more complex signal model that includes the variability in amplitude of each unit potential. Moreover, we propose the Markov Chain Monte Carlo (MCMC) simulation and a Bayesian minimum mean square error (MMSE) estimator by averaging on samples that converge in distribution to the joint posterior law. We prove the convergence property of this approach mathematically and we test the method representatively on intramuscular multiunit recordings. The results showed that its average accuracy in spike identification is greater than 90% for intramuscular signals with up to 8 concurrently active units. In addition to intramuscular signals, the method can be applied for spike sorting of other types of multiunit recordings.

Cited by 17Open access

HuertaLab/spike_sorting: Spike Sorting - BEM

HuertaLab · Zenodo (CERN European Organization for Nuclear Research) · 2021

This release contains the code for the Bioelectronic Medicine paper: Framework for acquisition and automated sorting of neural spikes from multielectrode recordings in freely-moving mice.

Cited by 0Open access

HuertaLab/spike_sorting: Spike Sorting - BEM

HuertaLab · Zenodo (CERN European Organization for Nuclear Research) · 2021

This release contains the code for the Bioelectronic Medicine paper: Framework for acquisition and automated sorting of neural spikes from multielectrode recordings in freely-moving mice.

Cited by 0Open access

Serial parameterization and spike sorting.

Matthew R. Baker (436704), Bryan T. Klassen (21560821), Michael A. Jensen (327073) et al. · Figshare · 2025

<b>(A)</b> Example spike sorted trace from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013184#pcbi.1013184.g003" target="_blank">Fig 3</a>. Multiple spikes occurring in the same spike window are missed by standard spike sorting. <b>(B)</b> The largest unit is parameterized to obtain for unit 1. <b>(C)</b> is subtracted from the filtered voltage trace and re-sorted using the same voltage threshold. <b>(D)</b> The second unit is parameterized. <b>(E)</b> parameterization of both units shows resolved overlapping spikes. <b>(F)</b> For the sample voltage trace, serial parameterization and spike sorting increased the number of detected spikes by over 900.

Cited by 0Open access