MEArec

MEArec simulates realistic extracellular spiking activity on Multi-Electrode Arrays (MEAs) to generate ground-truth datasets for spike sorting evaluation.

Key Features:

• Python implementation: MEArec is implemented in Python to perform computational simulations of extracellular recordings.
• Ground-truth dataset generation: Generates simulated extracellular recordings with known spike identities for validation of spike sorting algorithms.
• Customizable electrode designs: Simulates signals across various customizable electrode geometries and MEA layouts.
• Modeling complex neural activity: Reproduces bursting activity, spatio-temporal overlapping events, and signal drifts in simulated recordings.
• Customizable parameters: Provides parameter settings to tailor simulations to specific experimental conditions.
• Fast simulation: Enables rapid generation of datasets to support spike sorting testing and benchmarking.

Scientific Applications:

• Spike sorting evaluation: Provides ground-truth simulated recordings to quantify and compare performance of spike sorting algorithms.
• Algorithm development and benchmarking: Serves as a testbench for developing, tuning, and ranking spike sorting methods under controlled conditions that replicate experimental challenges.

Methodology:

Implemented in Python to simulate extracellular recordings on MEAs using customizable electrode designs and simulation parameters to model bursting activity, spatio-temporal overlaps, and signal drifts.