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A data driven system implies the need to integrate data ac- quisition and signal processing into the same system that will interact with this information. This can be done with general purpose proces- sors (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, clas- sifying 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 outper- formed PCs or DSPs by embedding algorithms directly in hardware. A data driven system is being developed for computational neuroscience that will be able to process an arbitrary number of real-time data streams and pro- vide a platform for neural modeling that can interact with these real-time data streams. The platform will be used to aid the discovery process where neural encoding schemes through which sensory information is represented and trans- mitted within a nervous system will be uncovered. The goal of the system is to enable real-time decoding of neural information streams and allow neuronal models to interact with living simple nervous systems. This will enable the in- tegration of experimental and theoretical neuroscience. Allowing experimental perturbation of neural signals while in transit between peripheral and central processing stages will provide an unprecedented degree of interactive control in the analysis of neural function, and could lead to major insights into the biolog- ical basis of neural computation. Integrating data acquisition, signal processing, and neural modeling requires an examination of the system architecture that is most suitable for this endeavor. This examination was done in the context of spike sorting, which is a necessary step in the acquisition and processing of neural signals. The spike sorting al- gorithm used for this study was based on Gaussian mixture models that have been found useful in signal processing applications, such as image processing, speech signal processing, and pattern recognition (1,2). The hardware platforms compared were the desktop PC, a parallel digital signal processing (DSP) im- plementation, and the use of field programmable gate arrays (FPGAs).

Developing a Data Driven System for Computational Neuroscience