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Spike Detection for Large Neural Populations Using High Density Multielectrode Arrays

Lookup NU author(s): Professor Evelyne SernagorORCiD

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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.

Publication metadata

Author(s): Muthmann JO, Amin H, Sernagor E, Maccione A, Panas D, Berdondini L, Bhalla US, Hennig MH

Publication type: Article

Publication status: Published

Journal: Frontiers in Neuroinformatics

Year: 2015

Volume: 9

Online publication date: 18/12/2015

Acceptance date: 24/11/2015

ISSN (print): 1662-5196

Publisher: Frontiers Research Foundation

URL: http://dx.doi.org/10.3389/fninf.2015.00028

DOI: 10.3389/fninf.2015.00028

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