Literature DB >> 25145954

Reconstruction of cell-electrode-adjacencies on multielectrode arrays.

Konrad Engel1, Sebastian Hanisch.   

Abstract

The multichannel recordings of signals of many cells cultivated on a multielectrode array (MEA) impose some challenging problems. A meanwhile classic problem is the separation of the recordings of a single electrode into classes of recordings where each class is caused by a single cell. This is the well-known spike sorting. A "dual" problem is the determination of the set of electrodes that record signals of a single cell. This set is called the neighborhood of the cell and has often more than one element if the MEA has a large number of electrodes with high density. A method for the reconstruction of the neighborhoods from the multichannel recordings is presented. Special effort is directed to a precise peak detection. For the evaluation of the algorithm, artificial data, obtained from an appropriate model of MEA recordings, are used. Because the artificial data provide a ground truth, an evaluation of the accuracy of the algorithm is possible. The algorithm works well for realistic parameters.

Mesh:

Year:  2014        PMID: 25145954     DOI: 10.1007/s10827-014-0524-6

Source DB:  PubMed          Journal:  J Comput Neurosci        ISSN: 0929-5313            Impact factor:   1.621


  14 in total

Review 1.  A review of methods for spike sorting: the detection and classification of neural action potentials.

Authors:  M S Lewicki
Journal:  Network       Date:  1998-11       Impact factor: 1.273

2.  Spike sorting based on discrete wavelet transform coefficients.

Authors:  J C Letelier; P P Weber
Journal:  J Neurosci Methods       Date:  2000-09-15       Impact factor: 2.390

3.  Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering.

Authors:  R Quian Quiroga; Z Nadasdy; Y Ben-Shaul
Journal:  Neural Comput       Date:  2004-08       Impact factor: 2.026

4.  Recording spikes from a large fraction of the ganglion cells in a retinal patch.

Authors:  Ronen Segev; Joe Goodhouse; Jason Puchalla; Michael J Berry
Journal:  Nat Neurosci       Date:  2004-10       Impact factor: 24.884

Review 5.  Independent component analysis at the neural cocktail party.

Authors:  G D Brown; S Yamada; T J Sejnowski
Journal:  Trends Neurosci       Date:  2001-01       Impact factor: 13.837

Review 6.  Towards reliable spike-train recordings from thousands of neurons with multielectrodes.

Authors:  Gaute T Einevoll; Felix Franke; Espen Hagen; Christophe Pouzat; Kenneth D Harris
Journal:  Curr Opin Neurobiol       Date:  2011-10-22       Impact factor: 6.627

7.  Automatic sorting for multi-neuronal activity recorded with tetrodes in the presence of overlapping spikes.

Authors:  Susumu Takahashi; Yuichiro Anzai; Yoshio Sakurai
Journal:  J Neurophysiol       Date:  2002-12-18       Impact factor: 2.714

8.  Applicability of independent component analysis on high-density microelectrode array recordings.

Authors:  David Jäckel; Urs Frey; Michele Fiscella; Felix Franke; Andreas Hierlemann
Journal:  J Neurophysiol       Date:  2012-04-04       Impact factor: 2.714

9.  A model-based spike sorting algorithm for removing correlation artifacts in multi-neuron recordings.

Authors:  Jonathan W Pillow; Jonathon Shlens; E J Chichilnisky; Eero P Simoncelli
Journal:  PLoS One       Date:  2013-05-03       Impact factor: 3.240

10.  How many neurons can we see with current spike sorting algorithms?

Authors:  Carlos Pedreira; Juan Martinez; Matias J Ison; Rodrigo Quian Quiroga
Journal:  J Neurosci Methods       Date:  2012-07-25       Impact factor: 2.390

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