title: Spike Detection for Large Neural Populations Using High Density Multielectrode Arrays.
abstract: An emerging generation of high-density microelectrode arrays (MEAs) is now capable of recording spiking activity simultaneously from thousands of neurons with closely spaced electrodes. Reliable spike detection and analysis in such recordings is challenging due to the large amount of raw data and the dense sampling of spikes with closely spaced electrodes. Here, we present a highly efficient, online capable spike detection algorithm, and an offline method with improved detection rates, which enables estimation of spatial event locations at a resolution higher than that provided by the array by combining information from multiple electrodes. Data acquired with a 4096 channel MEA from neuronal cultures and the neonatal retina, as well as synthetic data, was used to test and validate these methods. We demonstrate that these algorithms outperform conventional methods due to a better noise estimate and an improved signal-to-noise ratio (SNR) through combining information from multiple electrodes. Finally, we present a new approach for analyzing population activity based on the characterization of the spatio-temporal event profile, which does not require the isolation of single units. Overall, we show how the improved spatial resolution provided by high density, large scale MEAs can be reliably exploited to characterize activity from large neural populations and brain circuits.
authors.affiliation: Manipal UniversityManipal, India; Department of Neurobiology, National Centre for Biological Sciences, Tata Institute of Fundamental ResearchBangalore, India; School of Informatics, Institute for Adaptive and Neural Computation, University of EdinburghEdinburgh, UK.
authors.forename: Jens-Oliver
authors.initials: JO
authors.lastname: Muthmann
authors.affiliation: Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia Genova, Italy.
authors.forename: Hayder
authors.initials: H
authors.lastname: Amin
authors.affiliation: Institute of Neuroscience, Newcastle University Newcastle, UK.
authors.forename: Evelyne
authors.initials: E
authors.lastname: Sernagor
authors.affiliation: Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia Genova, Italy.
authors.forename: Alessandro
authors.initials: A
authors.lastname: Maccione
authors.affiliation: School of Informatics, Institute for Adaptive and Neural Computation, University of Edinburgh Edinburgh, UK.
authors.forename: Dagmara
authors.initials: D
authors.lastname: Panas
authors.affiliation: Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia Genova, Italy.
authors.forename: Luca
authors.initials: L
authors.lastname: Berdondini
authors.affiliation: Department of Neurobiology, National Centre for Biological Sciences, Tata Institute of Fundamental Research Bangalore, India.
authors.forename: Upinder S
authors.initials: US
authors.lastname: Bhalla
authors.affiliation: School of Informatics, Institute for Adaptive and Neural Computation, University of Edinburgh Edinburgh, UK.
authors.forename: Matthias H
authors.initials: MH
authors.lastname: Hennig
keywords: correlations; cultured neurons; microelectrode array; retina; spike detection