Unsupervised neural spike sorting for high-density microelectrode arrays with convolutive independent component analysis

Leibig, Christian; Wachtler, Thomas; Zeck, Günther

doi:10.1016/j.jneumeth.2016.06.006

Cited by 36 publications

(33 citation statements)

References 53 publications

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“…: Santhanam et al (2004); Vargas-Irwin & Donoghue (2007); Wood & Black (2008); Ventura (2009) ;Chah et al (2011); Bestel et al (2012); Barnett et al (2016)), but are computationally intensive, sensitive to changes in waveform due to electrode drift, and no ground truth is available. Exciting recent methods leverage high-density neural recordings to yield reliable single neuron isolation, but such sensors represent a minority of emerging technologies as they are optimized specifically for high-density recording within a small volume around a linear probe Harris et al (2016); Rossant et al (2016); Pachitariu et al (2016); Leibig et al (2016); Jun et al (2017a); Chung et al (2017). This requirement creates a trade off between spike sorting quality and measuring from a larger volume of tissue.…”

Section: Introductionmentioning

confidence: 99%

Accurate estimation of neural population dynamics without spike sorting

Trautmann

Stavisky

Lahiri

et al. 2017

Preprint

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A central goal of systems neuroscience is to relate an organism's neural activity to behavior. Neural population analysis often begins by reducing the dimensionality of the data to focus on the patterns most relevant to a given task. A major practical hurdle to data analysis is spike sorting, and this problem is growing rapidly as the number of neurons measured increases. Here, we investigate whether spike sorting is necessary to estimate neural dynamics. The theory of random projections suggests that we can accurately estimate the geometry of low-dimensional manifolds from a small number of linear projections of the data. We re-analyzed data from three previous studies and found that neural dynamics and scientific conclusions are quite similar using multi-unit threshold crossings in place of sorted neurons. This finding unlocks existing data for new analyses and informs the design and use of new electrode arrays for laboratory and clinical use. * Lead contact, Eric Trautmann etraut@stanford.edu

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Section: Introductionmentioning

confidence: 99%

Accurate estimation of neural population dynamics without spike sorting

Trautmann

Stavisky

Lahiri

et al. 2017

Preprint

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“…Datasets from thousands of electrodes necessitate a spike sorting algorithm that is extensively automated. Furthermore, the few algorithms that have been designed to process large-scale recordings have not been tested on data where one neuron is recorded by the large-scale recordings and simultaneously by another technique, so that the success rate of the spike sorting algorithm can be measured [Pachitariu et al, 2016, Leibig et al, 2016, Hilgen et al, 2016.…”

Section: Introductionmentioning

confidence: 99%

Fast and accurate spike sorting invitroandin vivofor up to thousands of electrodes

Yger

Esposito

et al. 2016

Preprint

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Understanding how assemblies of neurons encode information requires recording large populations of cells in the brain. In recent years, multi-electrode arrays and large silicon probes have been developed to record simultaneously from hundreds or thousands of electrodes packed with a high density. However, these new devices challenge the classical way to do spike sorting. Here we developed a new method to solve these issues, based on a highly automated algorithm to extract spikes from extracellular data, and show that this algorithm reached near optimal performance both in vitro and in vivo. The algorithm is composed of two main steps: 1) a "template-finding" phase to extract the cell templates, i.e. the pattern of activity evoked over many electrodes when one neuron fires an action potential; 2) a "template-matching" phase where the templates were matched to the raw data to find the location of the spikes. The manual intervention by the user was reduced to the minimal, and the time spent on manual curation did not scale with the number of electrodes. We tested our algorithm with large-scale data from in vitro and in vivo recordings, from 32 to 4225 electrodes. We performed simultaneous extracellular and patch recordings to obtain "ground truth" data, i.e. cases where the solution to the sorting problem is at least partially known. The performance of our algorithm was always close to the best expected performance. We thus provide a general solution to sort spikes from large-scale extracellular recordings.

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“…Analysis of the recordings using the CMOS-MEA were performed using CMOS MEA Tools, a software implementing the cICA algorithm (Leibig et al, 2016). Only well-isolated single units were considered (criterium: IsoBG > 5).…”

Section: Cmos-meamentioning

confidence: 99%

Human cerebrospinal fluid induces neuronal excitability changes in resected human neocortical and hippocampal brain slices

Wickham

Corna

Schwarz

et al. 2019

Preprint

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Human cerebrospinal fluid (hCSF) have proven advantageous over conventional medium when culturing both rodent and human brain tissue. Increased excitability and synchronicity, similar to the active state exclusively recorded in vivo, reported in rodent slice and cell-cultures with hCSF as recording medium, indicates properties of the hCSF not matched by the artificial cerebrospinal fluid (aCSF) commonly used for electrophysiological recording. To evaluate the possible importance of using hCSF as electrophysiological recording medium of human brain tissue, we compared the general excitability in ex vivo human brain tissue slice cultures during perfusion with hCSF and aCSF.For measuring the general activity from a majority of neurons within neocortical and hippocampal human ex vivo slices we used a microelectrode array (MEA) recording technique with 252 electrodes covering an area of 3.2 x 3.2 mm 2 and a second CMOS-based MEA with 4225 electrodes on a 2 x 2 mm 2 area for detailed mapping of action potential waveforms. We found that hCSF increase the number of active neurons and the firing rate of the neurons in the slices as well as increasing the numbers of bursts while leaving the duration of the bursts unchanged. Interestingly, not only an increase in the overall activity in the slices was observed, but a reconfiguration of the network functionality could be detected with specific activation and inactivation of subpopulations of neuronal ensembles. In conclusion, hCSF is an important component to consider for future human tissue studies, especially for experiments designed to mimic the in vivo situation.3

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Unsupervised neural spike sorting for high-density microelectrode arrays with convolutive independent component analysis

Cited by 36 publications

References 53 publications

Accurate estimation of neural population dynamics without spike sorting

Accurate estimation of neural population dynamics without spike sorting

Fast and accurate spike sorting invitroandin vivofor up to thousands of electrodes

Human cerebrospinal fluid induces neuronal excitability changes in resected human neocortical and hippocampal brain slices

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