Spike Detection for Large Neural Populations Using High Density Multielectrode Arrays

Muthmann, Jens-Oliver; Amin, Hayder; Sernagor, Evelyne; Maccione, Alessandro; Panas, Dagmara; Berdondini, Luca; Bhalla, Upinder S.; Hennig, Matthias H.

doi:10.3389/fninf.2015.00028

Cited by 56 publications

(73 citation statements)

References 44 publications

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“…Spike sorting Spikes were detected and sorted using the algorithms described in (20,29), using the HS2 software (https://github.com/mhhennig/HS2). Briefly, spikes were first detected as threshold crossings individually on each channel, and then merged into unique events based on spatial and temporal proximity.…”

Section: Synthetic Rgc Responsesmentioning

confidence: 99%

“…Unit selection. To avoid false negatives during detection, a low detection threshold was used (threshold 10, see (29) for definition), which led to an excessive high number putative units. To isolate well-sorted neurons from this set, several heuristic criteria were applied: First, to be included, the eccentricity of the ellipse defined by a bi-variate Gaussian fit to the spatial spike locations of each unit was thresholded to < 0.85, and the average of the two axes thresholded to below < 17% of the channel separation (7.14µm).…”

Section: Analysis Of Light Responses the Bias Index (30) Was Computementioning

confidence: 99%

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Non-parametric physiological classification of retinal ganglion cells in the mouse retina

Jouty

Hilgen

Sernagor

et al. 2018

Preprint

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Retinal ganglion cells, the sole output neurons of the retina, exhibit surprising diversity. A recent study reported over 30 distinct types in the mouse retina, indicating that the processing of visual information is highly parallelised in the brain. The advent of high density multi-electrode arrays now enables recording from many hundreds to thousands of neurons from a single retina. Here we describe a method for the automatic classification of large-scale retinal recordings using a simple stimulus paradigm and a spike train distance measure as a clustering metric. We evaluate our approach using synthetic spike trains, and demonstrate that major known cell types are identified in high-density recording sessions from the mouse retina with around 1000 retinal ganglion cells. A comparison across different retinas reveals substantial variability between preparations, suggesting pooling data across retinas should be approached with caution. As a parameter-free method, our approach is broadly applicable for cellular physiological classification in all sensory modalities. retinal ganglion cells | multi-electrode array | light responses | classification | spike distanceCorrespondence: m.hennig@ed.ac.uk

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Section: Synthetic Rgc Responsesmentioning

confidence: 99%

Section: Analysis Of Light Responses the Bias Index (30) Was Computementioning

confidence: 99%

Non-parametric physiological classification of retinal ganglion cells in the mouse retina

Jouty

Hilgen

Sernagor

et al. 2018

Preprint

Self Cite

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“…We have also evaluated spike sorting results using several other sorters: Kilosort (Pachitariu et al, 2016, Pachitariu, 2019), Spyking Circus Yger et al (2018, Mountainsort Chung et al (2017), JRClust andIronclust (Jun et al, 2017b), and Herding Spikes Muthmann et al (2015). Of this group, in our hands Kilosort consistently led to the best results on the retinal datasets considered here.…”

Section: Comparisons To Manual Sorts and Other Automated Spike-sortersmentioning

confidence: 72%

YASS: Yet Another Spike Sorter applied to large-scale multi-electrode array recordings in primate retina

Lee

Mitelut

Shokri

et al. 2020

Preprint

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Spike sorting is a critical first step in extracting neural signals from large-scale multi-electrode array (MEA) data. This manuscript presents several new techniques that make MEA spike sorting more robust and accurate. Our pipeline is based on an efficient multi-stage "triage-then-cluster-then-pursuit" approach that initially extracts only clean, high-quality waveforms from the electrophysiological time series by temporarily skipping noisy or "collided" events (representing two neurons firing synchronously). This is accomplished by developing a neural network detection and denoising method followed by efficient outlier triaging. The denoised spike waveforms are then used to infer the set of spike templates through nonparametric Bayesian clustering. We use a divide-andconquer strategy to parallelize this clustering step. Finally, we recover collided waveforms with matching-pursuit deconvolution techniques, and perform further split-and-merge steps to estimate additional templates from the pool of recovered waveforms. We apply the new pipeline to data recorded in the primate retina, where high firing rates and highly-overlapping axonal units provide a challenging testbed for the deconvolution approach; in addition, the well-defined mosaic structure of receptive fields in this preparation provides a useful quality check on any spike sorting pipeline. We show that our pipeline improves on the state-of-the-art in spike sorting (and outperforms manual sorting) on both real and semi-simulated MEA data with > 500 electrodes; open source code can be found at https://github.com/paninski-lab/yass. * Equal contribution authors ‡ DARPA Neural Engineering System Design program BAA-16-09 1 datastream as efficiently as possible. Finally, scalability must be a key consideration. To feasibly process the oncoming data deluge, we use parallel, scalable algorithms based on efficient data summarizations wherever possible and focus computational power on the "hard cases," using cheap fast methods to handle easy cases.To evaluate the resulting pipeline, we focus here on MEA data collected from the primate retina. This preparation is a useful spike sorting testbed for several important reasons. First, the two-dimensional MEA used here matches the approximately two-dimensional substrate of the retinal ganglion layer. Second, receptive fields of well-characterized retinal ganglion cell (RGC) types (e.g., ON parasols, OFF midgets, etc.) are known to approximately tile the visual field, providing useful side information for scoring different spike sorting pipelines. Third, many RGCs have moderately high firing rates and often have significant axonal projections that overlap with each other spatially on the MEA, making it challenging to demix spikes that overlap spatially and temporally from different RGCs * .We will first outline the methodology that forms the core of our pipeline in Section 2.1, then provide details of each module in the following subsections, and finally demonstrate the improvements in performance on 512-electrode primat...

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“…A commonly accepted qualitative evaluation method in the absence of ground truth is by percentage similarity estimation i.e. coefficient of determination and another important technique is to employ correlation analysis2442. To be adaptable for either of the analysis methods, we generate a pseudo temporal voltage information similar to synthetic data generation technique described in36 to compete with the original data.…”

Section: Results and Performance Evaluationmentioning

confidence: 99%

Unified selective sorting approach to analyse multi-electrode extracellular data

Veerabhadrappa

Lim

Nguyen

et al. 2016

Sci Rep

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Extracellular data analysis has become a quintessential method for understanding the neurophysiological responses to stimuli. This demands stringent techniques owing to the complicated nature of the recording environment. In this paper, we highlight the challenges in extracellular multi-electrode recording and data analysis as well as the limitations pertaining to some of the currently employed methodologies. To address some of the challenges, we present a unified algorithm in the form of selective sorting. Selective sorting is modelled around hypothesized generative model, which addresses the natural phenomena of spikes triggered by an intricate neuronal population. The algorithm incorporates Cepstrum of Bispectrum, ad hoc clustering algorithms, wavelet transforms, least square and correlation concepts which strategically tailors a sequence to characterize and form distinctive clusters. Additionally, we demonstrate the influence of noise modelled wavelets to sort overlapping spikes. The algorithm is evaluated using both raw and synthesized data sets with different levels of complexity and the performances are tabulated for comparison using widely accepted qualitative and quantitative indicators.

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Spike Detection for Large Neural Populations Using High Density Multielectrode Arrays

Cited by 56 publications

References 44 publications

Non-parametric physiological classification of retinal ganglion cells in the mouse retina

Non-parametric physiological classification of retinal ganglion cells in the mouse retina

YASS: Yet Another Spike Sorter applied to large-scale multi-electrode array recordings in primate retina

Unified selective sorting approach to analyse multi-electrode extracellular data

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