Design and Characterization of an Integrate-and-Fire Neural Recording System

A neuronal recording system for brain-machine interfaces (BMI) based on asynchronous biphasic pulse coding is described. A recording experiment comparing, in parallel, a commercial recording system (Tucker-Davis Technology) and the UF's custom solution (FWIRE) is set up to compare performance. The novel aspect of the UF system is that the analog signal is represented by an asynchronous pulse train, which provides a low-power, low-bandwidth, noise-resistant means for coding and transmission. Based on different front-end hardware settings, recording bandwidth and corresponding reconstruction accuracy can be varied. Taking advantage of neural firing features, the pulse-based approach requires less than 3K pulses/second to record a 25 KHz bandwidth signal from a hardware neural simulator. Recording performance has been characterized in the back-end signal processing with the spike sorting method. Two different spike sorting methods are proposed depending on different recording bandwidth constraints.