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Applicability of Independent Component Analysis on High-Density Microelectrode Array Recordings
- Source :
- Journal of Neurophysiology
- Publication Year :
- 2012
-
Abstract
- Emerging complementary metal oxide semiconductor (CMOS)-based, high-density microelectrode array (HD-MEA) devices provide high spatial resolution at subcellular level and a large number of readout channels. These devices allow for simultaneous recording of extracellular activity of a large number of neurons with every neuron being detected by multiple electrodes. To analyze the recorded signals, spiking events have to be assigned to individual neurons, a process referred to as “spike sorting.” For a set of observed signals, which constitute a linear mixture of a set of source signals, independent component (IC) analysis (ICA) can be used to demix blindly the data and extract the individual source signals. This technique offers great potential to alleviate the problem of spike sorting in HD-MEA recordings, as it represents an unsupervised method to separate the neuronal sources. The separated sources or ICs then constitute estimates of single-neuron signals, and threshold detection on the ICs yields the sorted spike times. However, it is unknown to what extent extracellular neuronal recordings meet the requirements of ICA. In this paper, we evaluate the applicability of ICA to spike sorting of HD-MEA recordings. The analysis of extracellular neuronal signals, recorded at high spatiotemporal resolution, reveals that the recorded data cannot be modeled as a purely linear mixture. As a consequence, ICA fails to separate completely the neuronal signals and cannot be used as a stand-alone method for spike sorting in HD-MEA recordings. We assessed the demixing performance of ICA using simulated data sets and found that the performance strongly depends on neuronal density and spike amplitude. Furthermore, we show how postprocessing techniques can be used to overcome the most severe limitations of ICA. In combination with these postprocessing techniques, ICA represents a viable method to facilitate rapid spike sorting of multidimensional neuronal recordings.
- Subjects :
- Retinal Ganglion Cells
Materials science
Physiology
Models, Neurological
Action Potentials
High density
In Vitro Techniques
Article
Mice
03 medical and health sciences
0302 clinical medicine
High spatial resolution
Animals
Computer Simulation
030304 developmental biology
Cyclic Nucleotide Phosphodiesterases, Type 6
Principal Component Analysis
0303 health sciences
Signal processing
business.industry
General Neuroscience
Signal Processing, Computer-Assisted
Multielectrode array
Microarray Analysis
Independent component analysis
Mice, Mutant Strains
Microelectrode
Nonlinear Dynamics
CMOS
Spike sorting
Linear Models
Optoelectronics
business
Microelectrodes
Algorithms
030217 neurology & neurosurgery
Subjects
Details
- Language :
- English
- ISSN :
- 00223077
- Database :
- OpenAIRE
- Journal :
- Journal of Neurophysiology
- Accession number :
- edsair.doi.dedup.....85dfa0a5c3c82e896082d37115d37fba
- Full Text :
- https://doi.org/10.1152/jn.01106.2011