Spike sorting: new trends and challenges of the era of high-density probes

Buccino, Alessio Paolo; Garcia, Samuel; Yger, Pierre

doi:10.31219/osf.io/jhau2

Cited by 8 publications

(5 citation statements)

References 77 publications

(119 reference statements)

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“…These recordings allowed us to unambiguously extract single-neuron responses, except during DEMS delivery where a stimulus artefact was preponderant. We could unambiguously identify bursts of action potentials (Fig 1C) without confounds induced by spike sorting algorithms (34). Inspired by how synaptic dynamics may read temporal codes (27), we studied burst codes by focusing on trial-averages of different spike timing patterns (Fig 1D): firing rates (FRs) are computed by trial-averages of any spikes, burst rates (BR) are computed by trial averages of bursts, event rates (ERs) are computed by trial-averages of singlets and bursts (i.e.…”

Section: Resultsmentioning

confidence: 96%

“…without confounds induced by spike sorting algorithms (34). Inspired by how synaptic dynamics may read temporal codes (27), we studied burst codes by focusing on trial-averages of different spike timing patterns (Fig 1D ): firing rates (FRs) are computed by trial-averages of any spikes, burst rates (BR) are computed by trial averages of bursts, event rates (ERs) are computed by trial-averages of singlets and bursts (i.e.…”

Section: Resultsmentioning

confidence: 99%

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Fast burst fraction transients convey information independent of the firing rate

Naud¹,

Wang²,

Friedenberger³

et al. 2022

Preprint

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Theories of attention and learning have hypothesized a central role for high-frequency bursting in cognitive functions, but experimental reports of burst-mediated representations in vivo have been limited. Here we used a novel demultiplexing approach to separate independent streams of information from considering neurons as having three possible states: silent, singlet- and burst-firing. We studied this ternary neural code in vivo while animals learned to behaviorally report direct electrical stimulation of the somatosensory cortex and found two acquired yet independent representations. One code, the event rate, represented the stimulus in a small fraction of cells and showed a small modulation upon detection errors. The other code, the burst fraction, correlated more globally with stimulation and more promptly responded to detection errors. Bursting modulation was potent and its time course evolved, even in cells that were considered unresponsive based on the firing rate. During the later stages of training, this modulation in bursting happened earlier, gradually aligning temporally with the representation in event rate. The alignment of bursting and event rate modulation sharpened firing rate coded representations, and was strongly associated behavioral accuracy. Thus a fine grain separation of spike timing patterns reveals two signals that accompany stimulus representations: an error signal that can be essential to guide learning and a sharpening signal that could enact top-down attention.

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Section: Resultsmentioning

confidence: 96%

Section: Resultsmentioning

confidence: 99%

Fast burst fraction transients convey information independent of the firing rate

Naud¹,

Wang²,

Friedenberger³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The optimal way of achieving this sorting is hindered by the lack of a ground truth and is still an active eld of research (see e.g., Buccino et al (2022)). In the times of single electrode recordings, it was common to sort the waveforms semi-manually, which usually involved the choice of multiple parameters and thresholds.…”

Section: Spike Sorting Does Not Ensure Synchrofact Removalmentioning

confidence: 99%

Detection and Removal of Hyper-synchronous Artifacts in Massively Parallel Spike Recordings

Oberste-Frielinghaus,

Morales-Gregorio,

Essink

et al. 2024

Preprint

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SummaryCurrent electrophysiology experiments often involve massively parallel recordings of neuronal activity using multi-electrode arrays. While researchers have been aware of artifacts arising from electric cross-talk between channels in setups for such recordings, systematic and quantitative assessment of the effects of those artifacts on the data quality has never been reported. Here we present, based on examination of electrophysiology recordings from multiple laboratories, that multielectrode recordings of spiking activity commonly contain extremely precise (at the data sampling resolution) spike coincidences far above the chance level. We derive, through modeling of the electric cross-talk, a systematic relation between the amount of such hyper-synchronous events (HSEs) in channel pairs and the correlation between the raw signals of those channels in the multi-unit activity frequency range (250-7500 Hz). Based on that relation, we propose a method to identify and exclude specific channels to remove artifactual HSEs from the data. We further demonstrate that the artifactual HSEs can severely affect various types of analyses on spike train data. Taken together, our results warn researchers to pay considerable attention to the presence of HSEs in spike train data and to make efforts to remove the artifactual ones from the data to avoid false results.

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“…While they do provide blackbox-type solutions to spike identification, sorting, generating raster plots following spike sorting and other measures, they may not offer enough customization and adaptability to different methods of viewing and analyzing the data (Bridges et al, 2018). Moreover, while different toolboxes and software platforms provide different functionality, there are benefits and limitations related to the scalability of algorithms for large-scale data, and new paradigms are constantly evolving to exploit the vast potential of these recordings (Mahmud et al, 2012;Diggelmann et al, 2018;Sedaghat-Nejad et al, 2021;Buccino et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Xenon LFP Analysis Platform Is a Novel Graphical User Interface for Analysis of Local Field Potential From Large-Scale MEA Recordings

2022

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High-density multi-electrode array (HD-MEA) has enabled neuronal measurements at high spatial resolution to record local field potentials (LFP), extracellular action potentials, and network-wide extracellular recording on an extended spatial scale. While we have advanced recording systems with over 4,000 electrodes capable of recording data at over 20 kHz, it still presents computational challenges to handle, process, extract, and view information from these large recordings. We have created a computational method, and an open-source toolkit built in Python, rendered on a web browser using Plotly’s Dash for extracting and viewing the data and creating interactive visualization. In addition to extracting and viewing entire or small chunks of data sampled at lower or higher frequencies, respectively, it provides a framework to collect user inputs, analyze channel groups, generate raster plots, view quick summary measures for LFP activity, detect and isolate noise channels, and generate plots and visualization in both time and frequency domain. Incorporated into our Graphical User Interface (GUI), we also created a novel seizure detection method, which can be used to detect the onset of seizures in all or a selected group of channels and provide the following measures of seizures: distance, duration, and propagation across the region of interest. We demonstrate the utility of this toolkit, using datasets collected from an HD-MEA device comprising of 4,096 recording electrodes. For the current analysis, we demonstrate the toolkit and methods with a low sampling frequency dataset (300 Hz) and a group of approximately 400 channels. Using this toolkit, we present novel data demonstrating increased seizure propagation speed from brain slices of Scn1aHet mice compared to littermate controls. While there have been advances in HD-MEA recording systems with high spatial and temporal resolution, limited tools are available for researchers to view and process these big datasets. We now provide a user-friendly toolkit to analyze LFP activity obtained from large-scale MEA recordings with translatable applications to EEG recordings and demonstrate the utility of this new graphic user interface with novel biological findings.

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Spike sorting: new trends and challenges of the era of high-density probes

Cited by 8 publications

References 77 publications

Fast burst fraction transients convey information independent of the firing rate

Fast burst fraction transients convey information independent of the firing rate

Detection and Removal of Hyper-synchronous Artifacts in Massively Parallel Spike Recordings

Xenon LFP Analysis Platform Is a Novel Graphical User Interface for Analysis of Local Field Potential From Large-Scale MEA Recordings

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