Abstract
Neuronal spike sorting algorithms are designed to retrieve neuronal network activity on a single-cell level from extracellular multiunit recordings with Microelectrode Arrays (MEAs). In typical analysis of MEA data, one spike sorting algorithm is applied indiscriminately to all electrode signals. However, this approach neglects the dependency of algorithms' performances on the neuronal signals properties at each channel, which require data-centric methods. Moreover, sorting is commonly performed off-line, which is time and memory consuming and prevents researchers from having an immediate glance at ongoing experiments. The aim of this work is to provide a versatile framework to support the evaluation and comparison of different spike classification algorithms suitable for both off-line and on-line analysis. We incorporated different spike sorting “building blocks” into a Matlab-based software, including 4 feature extraction methods, 3 feature clustering methods, and 1 template matching classifier. The framework was validated by applying different algorithms on simulated and real signals from neuronal cultures coupled to MEAs. Moreover, the system has been proven effective in running on-line analysis on a standard desktop computer, after the selection of the most suitable sorting methods. This work provides a useful and versatile instrument for a supported comparison of different options for spike sorting towards more accurate off-line and on-line MEA data analysis.
Highlights
Simultaneous multisite recordings using Microelectrode Arrays (MEAs) coupled to cultured neuronal networks are a widely applied approach in the field of in vitro electrophysiology [1,2,3]
Our aim was to provide a framework for an easy comparison of different spike sorting algorithms on the same data which would be suitable for off-line and on-line data analysis
The implemented toolbox integrates different spike sorting blocks, which have been selected from the literature
Summary
Simultaneous multisite recordings using Microelectrode Arrays (MEAs) coupled to cultured neuronal networks are a widely applied approach in the field of in vitro electrophysiology [1,2,3]. This technology overcomes the drawbacks of single-cell recording techniques, by sampling the electrical activity of a neuronal culture from multiple sites in a noninvasive way. This allows long-term studies of extracellular neuronal action potentials (i.e., spikes) and high frequency sequences of spikes (i.e., bursts) recorded at each electrode. Applications where the focus is on the spike timing analysis [6] or that require discrimination between the activities of neurons of different origin [7,8,9] are examples of studies that benefit from spike sorting
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