Spike Detection for Large Neural Populations Using High Density Multielectrode Arrays

Jens-Oliver Muthmann,Evelyne Sernagor,Luca Berdondini,Alessandro Maccione,Matthias H Hennig,Upinder S Bhalla,Hayder Amin,Dagmara Panas

doi:10.3389/fninf.2015.00028

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.

Highlights

Emerging generations of high-density microelectrode arrays (MEAs) based on CMOS technology allow recording extracellular signals from neural population activity with unprecedented detail (Eversmann et al, 2003; Berdondini et al, 2005; Hutzler et al, 2006; Maccione et al, 2014)
All code to replicate the analysis shown in this paper is provided at https:// github.com/martinosorb/herding-spikes, and example datasets at https://portal.carmen.org.uk/#link=URN:LSID:portal.carmen. org.uk:metadata
To facilitate adaptation of the method for other systems we studied that dependence in a simplified setting in Supplementary Figure 1

Summary

Introduction

Emerging generations of high-density microelectrode arrays (MEAs) based on CMOS technology allow recording extracellular signals from neural population activity with unprecedented detail (Eversmann et al, 2003; Berdondini et al, 2005; Hutzler et al, 2006; Maccione et al, 2014). These devices are capable of simultaneously recording extracellular activity with thousands of channels at near cellular resolution, providing an unbiased sample of neural activity in a variety of in vitro preparations. Even though distant current sources will be picked up by many electrodes, their detection is impaired because of a higher intrinsic noise level

Objectives

Methods

Results