FPGA-based neural probe positioning to improve spike sorting with OSort algorithm

Abstract

The extracellular measurement of brain electrical activity contains local field potentials and mixtures of action potentials generated by the neurons. It is essential to determine which individual neuron produces the recorded unit activity, so spike sorting methods are used. High channel-count neural probes are capable of recording the activity of large neural ensembles from up to more than hundred individual brain positions simultaneously, pose an even greater challenge for spike sorting applied on general-purpose hardware. Real-time clinical applications could greatly benefit from a hardware-accelerated data processing, especially in the case of Field-Programmable Gate Arrays (FPGAs) or Application Specific Integrated Circuits (ASICs), which are energy-efficient compared to traditional CPUs or GPUs, and can significantly reduce the computation time required to process large amounts of high-dimensional data. In this paper, we present a real-time FPGA-based implementation of a multi-channel Online Sorting (OSort) algorithm to pre-cluster neural data. Based on this pre-processing the neurobiologists can fine-tune the position of neural probe and improve the efficiency of offline spike sorting.