Neural spikes digital detector/sorting on FPGA

Abstract

This paper presents the results of a multidisciplinary experiment where the electrical activity of a rat hippocampus cultured neurons population has been detected and mapped by an advanced FPGA spike-sorting algorithm. Neurons are growth over a silicon chip that is thus capacitively coupled with neuronal cells. Due to noise power coming from bio-silicon interface and analog electronics signal processing, the Action Potentials detection intrinsically needs advanced noise rejection algorithms which are often software/off-line implemented. This approach disables instantaneous detection of neural spikes and cannot be obviously used for real-time electrical stimulation. In this scenario, this paper presents a proper FPGA system able to separate relevant neuronal cells potentials from noise. The FPGA output signals provide real time spatial mapping of biosensor electrical activity, noise and synchronous neural network activity.