Developing a Data Driven System for Computational Neuroscience

Abstract

A data driven system implies the need to integrate data acquisition and signal processing into the same system that will interact with this information. This can be done with general purpose processors (PCs), digital signal processors (DSPs), or more recently with field programmable gate arrays (FPGAs). In a computational neuroscience system that will interact with neural data recorded in real-time, classifying action potentials, commonly referred to as spike sorting, is an important step in this process. A comparison was made between using a PC, DSPs, and FPGAs to train a spike sorting system using Gaussian Mixture Models. The results show that FPGAs can significantly outperformed PCs or DSPs by embedding algorithms directly in hardware.

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