scholarly journals ProbeInterface: a unified framework for probe handling in extracellular electrophysiology

2021 ◽  
Author(s):  
Samuel Garcia ◽  
Julia Sprenger ◽  
Tahl Holtzman ◽  
Alessio Buccino
Keyword(s):  
Neuronal Activity ◽  
Spike Sorting ◽  
Electrode Arrays ◽  
End User ◽  
Neural Probes ◽  
Unified Framework ◽  
Specific Data ◽  
Neural Recordings ◽  
Channel Electrode ◽  
Probe Mapping

Recording neuronal activity with penetrating extracellular multi-channel electrode arrays, more commonly known as neural probes, is one of the most widespread approaches to probe neuronal activity. Despite a plethora of available extracellular probe designs, the time-consuming process of mapping of electrode channel order and relative geometries, as required by spike-sorting software is invariably left to the end-user. Consequently, this manual process is prone to mis-mapping mistakes, which in turn lead to undesirable spike-sorting errors and inefficiencies.Here we introduce ProbeInterface, an open-source project that aims to unify neural probe metadata descriptions by removing the manual step of probe mapping prior to spike-sorting for the analysis of extracellular neural recordings. ProbeInterface is first of all a Python API, which enables users to create and visualize probes and probe groups at any required complexity level. Second, ProbeInterface facilitates the generation of comprehensive wiring description ina reproducible fashion for any specific data-acquisition setup, which usually involves the use of a recording probe, a headstage, adapters, and an acquisition system. Third, we collaborate with probe manufacturers to compile an open library of available probes, which can be downloaded at run time using our Python API. Finally, with ProbeInterface we define a file format for probe handling which includes all necessary information for a FAIR probe description and is compatiblewith and complementary to other open standards in neuroscience.

scholarly journals Kilosort: realtime spike-sorting for extracellular electrophysiology with hundreds of channels

2016 ◽  
Author(s):  
Marius Pachitariu ◽  
Nicholas Steinmetz ◽  
Shabnam Kadir ◽  
Matteo Carandini ◽  
Harris Kenneth D.
Keyword(s):  
Large Scale ◽  
Superior Performance ◽  
Spike Sorting ◽  
Electrode Arrays ◽  
Manual Curation ◽  
Electrophysiological Recordings ◽  
Dimensional Approximation ◽  
Low Dimensional ◽  
Channel Electrode

AbstractAdvances in silicon probe technology mean that in vivo electrophysiological recordings from hundreds of channels will soon become commonplace. To interpret these recordings we need fast, scalable and accurate methods for spike sorting, whose output requires minimal time for manual curation. Here we introduce Kilosort, a spike sorting framework that meets these criteria, and show that it allows rapid and accurate sorting of large-scale in vivo data. Kilosort models the recorded voltage as a sum of template waveforms triggered on the spike times, allowing overlapping spikes to be identified and resolved. Rapid processing is achieved thanks to a novel low-dimensional approximation for the spatiotemporal distribution of each template, and to batch-based optimization on GPUs. A novel post-clustering merging step based on the continuity of the templates substantially reduces the requirement for subsequent manual curation operations. We compare Kilosort to an established algorithm on data obtained from 384-channel electrodes, and show superior performance, at much reduced processing times. Data from 384-channel electrode arrays can be processed in approximately realtime. Kilosort is an important step towards fully automated spike sorting of multichannel electrode recordings, and is freely available (github.com/cortex-lab/Kilosort).

Component analysis reveals sharp tuning of the local field potential in the guinea pig auditory cortex

2013 ◽  
Vol 109 (1) ◽  
pp. 261-272 ◽  
Author(s):  
Alain de Cheveigné ◽  
Jean-Marc Edeline ◽  
Quentin Gaucher ◽  
Boris Gourévitch
Keyword(s):  
Auditory Cortex ◽  
Local Field ◽  
Receptive Fields ◽  
Field Potential ◽  
Component Analysis ◽  
Electrode Arrays ◽  
Linear Combinations ◽  
Spectrotemporal Receptive Fields ◽  
Analysis Technique ◽  
Channel Electrode

Local field potentials (LFPs) recorded in the auditory cortex of mammals are known to reveal weakly selective and often multimodal spectrotemporal receptive fields in contrast to spiking activity. This may in part reflect the wider “listening sphere” of LFPs relative to spikes due to the greater current spread at low than high frequencies. We recorded LFPs and spikes from auditory cortex of guinea pigs using 16-channel electrode arrays. LFPs were processed by a component analysis technique that produces optimally tuned linear combinations of electrode signals. Linear combinations of LFPs were found to have sharply tuned responses, closer to spike-related tuning. The existence of a sharply tuned component implies that a cortical neuron (or group of neurons) capable of forming a linear combination of its inputs has access to that information. Linear combinations of signals from electrode arrays reveal information latent in the subspace spanned by multichannel LFP recordings and are justified by the fact that the observations themselves are linear combinations of neural sources.

scholarly journals Endogenous cholinergic tone modulates spontaneous network level neuronal activity in primary cortical cultures grown on multi-electrode arrays

2013 ◽  
Vol 14 (1) ◽  
pp. 38 ◽  
Author(s):  
Mark W Hammond ◽  
Dimitris Xydas ◽  
Julia H Downes ◽  
Giovanna Bucci ◽  
Victor Becerra ◽  
...  
Keyword(s):  
Neuronal Activity ◽  
Electrode Arrays ◽  
Cortical Cultures ◽  
Cholinergic Tone

scholarly journals YASS: Yet Another Spike Sorter

2017 ◽  
Author(s):  
JinHyung Lee ◽  
David Carlson ◽  
Hooshmand Shokri ◽  
Weichi Yao ◽  
Georges Goetz ◽  
...  
Keyword(s):  
Dirichlet Process ◽  
Large Scale ◽  
Matching Pursuit ◽  
Spike Sorting ◽  
Dirichlet Process Mixture ◽  
Electrode Arrays ◽  
Neural Signals ◽  
Model Framework ◽  
Multi Stage ◽  
Computational Bottleneck

AbstractSpike sorting is a critical first step in extracting neural signals from large-scale electrophysiological data. This manuscript describes an efficient, reliable pipeline for spike sorting on dense multi-electrode arrays (MEAs), where neural signals appear across many electrodes and spike sorting currently represents a major computational bottleneck. We present several new techniques that make dense MEA spike sorting more robust and scalable. 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 method followed by efficient outlier triaging. The clean waveforms are then used to infer the set of neural spike waveform templates through nonparametric Bayesian clustering. Our clustering approach adapts a “coreset” approach for data reduction and uses efficient inference methods in a Dirichlet process mixture model framework to dramatically improve the scalability and reliability of the entire pipeline. The “triaged” waveforms are then finally recovered with matching-pursuit deconvolution techniques. The proposed methods improve on the state-of-the-art in terms of accuracy and stability on both real and biophysically-realistic simulated MEA data. Furthermore, the proposed pipeline is efficient, learning templates and clustering much faster than real-time for a ≃ 500-electrode dataset, using primarily a single CPU core.

scholarly journals Deuterated Glutamate-Mediated Neuronal Activity on Micro-Electrode Arrays

Micromachines ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 830
Author(s):  
Wataru Minoshima ◽  
Kyoko Masui ◽  
Tomomi Tani ◽  
Yasunori Nawa ◽  
Satoshi Fujita ◽  
...  
Keyword(s):  
Spontaneous Activity ◽  
Neuronal Activity ◽  
Hippocampal Neurons ◽  
Neuronal Networks ◽  
Microelectrode Array ◽  
Mammalian Brain ◽  
Electrode Arrays ◽  
Physiological Properties ◽  
Micro Electrode Arrays

The excitatory synaptic transmission is mediated by glutamate (GLU) in neuronal networks of the mammalian brain. In addition to the synaptic GLU, extra-synaptic GLU is known to modulate the neuronal activity. In neuronal networks, GLU uptake is an important role of neurons and glial cells for lowering the concentration of extracellular GLU and to avoid the excitotoxicity. Monitoring the spatial distribution of intracellular GLU is important to study the uptake of GLU, but the approach has been hampered by the absence of appropriate GLU analogs that report the localization of GLU. Deuterium-labeled glutamate (GLU-D) is a promising tracer for monitoring the intracellular concentration of glutamate, but physiological properties of GLU-D have not been studied. Here we study the effects of extracellular GLU-D for the neuronal activity by using primary cultured rat hippocampal neurons that form neuronal networks on microelectrode array. The frequency of firing in the spontaneous activity of neurons increased with the increasing concentration of extracellular GLU-D. The frequency of synchronized burst activity in neurons increased similarly as we observed in the spontaneous activity. These changes of the neuronal activity with extracellular GLU-D were suppressed by antagonists of glutamate receptors. These results suggest that GLU-D can be used as an analog of GLU with equivalent effects for facilitating the neuronal activity. We anticipate GLU-D developing as a promising analog of GLU for studying the dynamics of glutamate during neuronal activity.

scholarly journals Electrical Stimulus Artifact Cancellation and Neural Spike Detection on Large Multi-Electrode Arrays

2016 ◽  
Author(s):  
Gonzalo E. Mena ◽  
Lauren E. Grosberg ◽  
Sasidhar Madugula ◽  
Paweł Hottowy ◽  
Alan Litke ◽  
...  
Keyword(s):  
Electrical Stimulation ◽  
High Resolution ◽  
Action Potentials ◽  
Process Model ◽  
Error Rates ◽  
Neural Interfaces ◽  
Spike Sorting ◽  
Electrode Arrays ◽  
Retinal Prostheses ◽  
Sorting Process

AbstractSimultaneous electrical stimulation and recording using multi-electrode arrays can provide a valuable technique for studying circuit connectivity and engineering neural interfaces. However, interpreting these measurements is challenging because the spike sorting process (identifying and segregating action potentials arising from different neurons) is greatly complicated by electrical stimulation artifacts across the array, which can exhibit complex and nonlinear waveforms, and overlap temporarily with evoked spikes. Here we develop a scalable algorithm based on a structured Gaussian Process model to estimate the artifact and identify evoked spikes. The effectiveness of our methods is demonstrated in both real and simulated 512-electrode recordings in the peripheral primate retina with single-electrode and several types of multi-electrode stimulation. We establish small error rates in the identification of evoked spikes, with a computational complexity that is compatible with real-time data analysis. This technology may be helpful in the design of future high-resolution sensory prostheses based on tailored stimulation (e.g., retinal prostheses), and for closed-loop neural stimulation at a much larger scale than currently possible.Author SummarySimultaneous electrical stimulation and recording using multi-electrode arrays can provide a valuable technique for studying circuit connectivity and engineering neural interfaces. However, interpreting these recordings is challenging because the spike sorting process (identifying and segregating action potentials arising from different neurons) is largely stymied by electrical stimulation artifacts across the array, which are typically larger than the signals of interest. We develop a novel computational framework to estimate and subtract away this contaminating artifact, enabling the large-scale analysis of responses of possibly hundreds of cells to tailored stimulation. Importantly, we suggest that this technology may also be helpful for the development of future high-resolution neural prosthetic devices (e.g., retinal prostheses).

scholarly journals Author response: SpikeInterface, a unified framework for spike sorting

2020 ◽  
Author(s):  
Alessio P Buccino ◽  
Cole L Hurwitz ◽  
Samuel Garcia ◽  
Jeremy Magland ◽  
Joshua H Siegle ◽  
...  
Keyword(s):  
Author Response ◽  
Spike Sorting ◽  
Unified Framework

scholarly journals Evaluation and resolution of many challenges of neural spike-sorting: a new sorter

2021 ◽  
Author(s):  
Nathan J. Hall ◽  
David J. Herzfeld ◽  
Stephen G. Lisberger
Keyword(s):  
Failure Modes ◽  
Ground Truth ◽  
Spike Sorting ◽  
Neural Data ◽  
Neural Recordings ◽  
Recording Session ◽  
Neural Spike

AbstractWe evaluate existing spike sorters and present a new one that resolves many sorting challenges. The new sorter, called “full binary pursuit” or FBP, comprises multiple steps. First, it thresholds and clusters to identify the waveforms of all unique neurons in the recording. Second, it uses greedy binary pursuit to optimally recognize the spike events in the original voltages. Third, it resolves spike events that are described more accurately as the superposition of spikes from two other neurons. Fourth, it resolves situations where the recorded neurons drift in amplitude or across electrode contacts during a long recording session. Comparison with other sorters on real and simulated ground-truth datasets reveals many of the failure modes of spike sorters. We suggest a set of post-sorting analyses that can improve the veracity of neural recordings by minimizing the intrusion of those failure modes into analysis and interpretation of neural data.

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