FPGA-based real-time multichannel neural dataset generation

摘要

Miniaturized voltage sensors (electrodes) implanted into the brain tissue are capable of recording the brief electrical impulses (spikes) of neurons located close to the electrode sites. To investigate the activity of individual neurons and discriminate spikes generated by different neurons a technique called spike sorting can be applied on the recorded data. However, the performance of current spike sorting methods is challenged by multichannel neural data recorded with high-density, high-channel count silicon probes developed recently. Our group started to develop an FPGA-based solution to accelerate the clustering of spikes detected in high-channel count neural recordings. It is a crucial step of the development to validate the performance of the clustering algorithm. This can be achieved by using ground truth datasets where the exact time of spikes fired by different single units are known. In this paper we present an FPGA-based architecture for real-time generation of multichannel hybrid ground truth datasets, which will be used for the validation of our FPGA-based clustering algorithm.