‘ SpikeNburst ’ and ‘ Nicespike ’: Advanced Tools for Enhancing and Accelerating In Vitro High-Density Electrophysiology Analysis

High-density multi-electrode arrays (HD-MEAs) have revolutionized electrophysiology by enabling the recording of neuronal activity with unprecedented spatial and temporal resolution. However, analysing these large-scale datasets poses significant challenges, including artefact removal, spike sorting, and accurate assessments of neuronal synchronization. Here, we present two Python-based tools, ‘ spikeNburst ’ and ‘ nicespike ’, designed to address these challenges and provide a scalable solution for the comprehensive analysis of HD-MEA recordings.

The spikeNburst tool integrates advanced methodologies for spike train filtering, burst and network burst detection, and synchronization analysis, and we implemented a full analysis pipeline in the nicespike tool, which includes GPU-accelerated spike sorting using template matching with Kilosort to accurately identify neuronal units spanning multiple electrodes. Together, these tools enable more precise analyses by mitigating redundancy and overestimation inherent in single-channel approaches. Their graphical user and command-line interfaces ensure accessibility for diverse user needs.

We validated the tools on neuronal culture recordings, demonstrating their ability to identify somatic and dendritic features of neuronal units, characterize bursting behaviour, and quantify synchronization at both unit and network levels. By addressing critical limitations of existing methods, spikeNburst and nicespike provide a robust, scalable, and user-friendly framework for HD-MEA data analysis, advancing our ability to study neural network dynamics and single-cell activity in detail.