Improved noise reduction in single fiber auditory neural responses using template subtraction

When recording single-unit responses from neural systems, a common problem is the accurate detection of spikes (action potentials) in the presence of competing unwanted (noise) signals. While some sources of noise can be readily dealt with through filtering or established “template subtraction” techniques, other sources present a more difficult problem. In particular, noise components introduced by power supplies, which contain harmonics of the power-line frequency, can be particularly troublesome in that they can mimic the shape of the desired spikes. The aforementioned standard techniques typically fail to effectively deal with such “noise”. In this study, we propose the use of a novel template-subtraction scheme that involves estimating the power-line noise waveform and using cross-correlation techniques to subtract it from the recordings. This technique requires two key steps: (1) cross-correlation analysis of each recorded waveform to extract a robust representation of the power-line noise waveform and (2) a second level of cross-correlation to successfully subtract that representation from each recorded waveform. This paper describes this algorithm and provides examples of its implementation using actual recorded waveforms that were contaminated with these power-line noise signals. An improvement (reduction) in the noise level is reported, as are suggestions for future implementation of this strategy.