Spike sorting with support vector machines

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.

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.

Support Vector Machines (SVMs) are a type of supervised machine learning algorithm widely used for classification tasks. In contrast to traditional methods that split the data into separate training and testing sets, here we propose an innovative approach where subsets of the original data are randomly selected to train the model multiple times. This iterative training process aims to identify a representative data subset, leading to improved inferences about the population. Additionally, we introduce a novel distance-based kernel specifically designed for binary-type features based on a similarity matrix that efficiently handles both binary and multi-class classification problems. Computational experiments on publicly available datasets of varying sizes demonstrate that our proposed method significantly outperforms existing approaches in terms of classification accuracy. Furthermore, the distance-based kernel achieves superior performance compared to other well-known kernels from the literature and those used in previous studies on the same datasets. These findings validate the effectiveness of our proposed classification method and distance-based kernel for SVMs. By leveraging random subset selection and a unique kernel design, we achieve notable improvements in classification accuracy. These results have significant implications for diverse classification problems in Machine Learning and data analysis.

This paper proposed a design of a closed-loop, bi-directional brain-machine-interface (BMI). The proposed chip consists of 16-channel neural acquisition, 8-channel neural stimulation, action potential detection, feature extraction and support vector machine (SVM) for spike sorting. A closed-loop control strategy is utilized to trigger different stimulation pattern according to different detected spike categories. The 16-channel neural signal acquisition is comprised of 16-channel low noise amplifier (LNA) which shares a single Programmable Gain Amplifier (PGA) draws a total current of 218uA under a power supply of 3.3V. Arbitrary combination is realized for the stimulator channel control. The frequency, the output current amplitude, the pulse width and the burst number are all tunable. The first and second derivative extrema method is applied to the detected action potential before spike sorting, realizing a great reduction on the data to be processed in the SVM. A single two-class sorting module is implemented as a results from the tradeoff between area and latency. More than two-class spike sorting is realized by a repeating of the two-class sorting procedure. The design has been fabricated in TSMC 180nm HV COMS process, occupying a silicon area of 5mm × 2mm.

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.

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%).

This chapter explains the important concepts and basics requirements of support vector machine (SVM) and apply it to real-life models. It focuses on forecasting the outcome values of time series using an SVM. The objectives of statistical learning are understanding and forecasting. SVM is used as a popular tool for classification of problems in machine learning. SVMs simply separate the given data into different classes by finding the line or hyper-plane and separate the kernel supports to find the hyper-plane in the higher dimension. In regression problems, the support vector regression works on the same principle as SVM. To reduce computational complexity in SVM, J. A. K. Suykensand and J. Vandewalle introduced the LS-SVM classifier in 1999. Though LS-SVM is suitable for time series forecasting, to make it more convenient, it has been modified to DLS-SVM using a slightly improved computation technique.

The problem of optimization in machine learning is well established but it entails several approximations. The theory of Hilbert spaces, which is principled and well established, helps solve the representation problem in machine learning by providing a rich (universal) class of functions where the optimization can be conducted. Working with functions is cumbersome, but for the class of reproducing kernel Hilbert spaces (RKHSs) it is still manageable provided the algorithm is restricted to inner products. The best example is the support vector machine (SVM), which is a batch mode algorithm that uses a very efficient (supralinear) optimization procedure. However, the problem of SVMs is that they display large memory and computational complexity. For the large-scale data limit, SVMs are restrictive because for fast operation the Gram matrix, which increases with the square of the number of samples, must fit in computer memory. The computation in this best-case scenario is also proportional to number of samples square. This is not specific to the SVM algorithm and is shared by kernel regression. There are also other relevant data processing scenarios such as streaming data (also called a time series) where the size of the data is unbounded and potentially nonstationary, therefore batch mode is not directly applicable and brings added difficulties.

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.

: Our laboratory has been working towards completion of an adaptive clustering spike-sorting circuit and the acquisition of bat vocalizations from a flying bat. In this summary for the project, we show some basic operation of the spike-sorting chip and discuss issues for further development. We have successfully flown a radio telemetry microphone on a bat and recorded good signals for the beginning of a new series of bat behavioral experiments.

The noise in the acquired hyperspectral image (HSI) is generally assumed as additive white Gaussian noise (WGN), while the estimation of the noise in each band of the real-world HSI shows that the noise is not white. Reducing the additive non-white noise is an important preprocessing step to further analyze the information in the HSIs by classification. A PMWF method, prewhitening and MWF (multidimensional Wiener filtering), was suggested and the non-white noise could be whitened by a pre-whitening procedure. While this method is time-consuming due to both the pre-whitening step and the estimation of three ranks of the MWF method. In this paper, we introduce a powerful multilinear algebra model, named parallel factor analysis (PARAFAC), which has only one rank and need not the pre-whitening procedure. To improve the classification, the rank of PARAFAC decomposition is estimated according to the maximum of the overall accuracy (OA) of the support vector machine (SVM) classification. The experiment results show that the PARAFAC model has high efficiency in the reduction of non-white noise and is a preferable preprocessing method for the accuracy improvement of the SVM classification.

Accurate spike sorting is an important issue for neuroscientific and neuroprosthetic applications. The sorting of spikes depends on the features extracted from the neural waveforms, and a better sorting performance usually comes with a higher sampling rate (SR). However for the long duration experiments on free-moving subjects, the miniaturized and wireless neural recording ICs are the current trend, and the compromise on sorting accuracy is usually made by a lower SR for the lower power consumption. In this paper, we implement an on-chip spike sorting processor with integrated interpolation hardware in order to improve the performance in terms of power versus accuracy. According to the fabrication results in 90nm process, if the interpolation is appropriately performed during the spike sorting, the system operated at the SR of 12.5 k samples per second (sps) can outperform the one not having interpolation at 25 ksps on both accuracy and power.

Neural spike sorting is used to classify neural spike signals based on neuron type and so is an essential step in decoding brain signals. Because of its computational complexity, spike sorting is generally carried out offline or, at least, using a transmitted signal. In contrast, in-brain spike sorting would reduce the data that needs to be transmitted by orders of magnitude with a corresponding reduction in transmission power. This would enable real-time wireless neural recordings. In this paper, we design and characterize a hardware prototype for in-brain spike sorting. Our design is able to reduce the wireless transmission power by a factor of over 200 over direct transmission. Also, compared with the current state-of-the-art, our design increases the sorting accuracy from 75% to 93% while remaining within hard constraints for power, power density, and real-time processing.

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).

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.