Extraction, discrimination and analysis of single-neuron signals by a personal-computer-based algorithm.

We communicated a computer algorithm that is capable of concurrently extracting, discriminating and analyzing single-neuron signals from adjacent neurons, particularly those with poor signal-to-noise ratio or contaminated by 60-Hz noise and/or baseline drift. Based on a continuous process of differentiation and peak-to-peak amplitude discrimination, our algorithm provided a two-dimensional amplitude histogram that readily distinguishes the clusters of spike signals representing different neurons. The inclusion of a time domain in our three-dimensional amplitude histogram further allowed us to simultaneously evaluate the temporal responses of neighboring cells to the same experimental manipulation. In addition to retaining many of the advanced features of existing extraction and discrimination procedures, this method offered the benefits of being efficient, requires minimal supervision and operates in real time even during long-term recording. Above all, it is cost effective because it is purely software based and only requires a PC-AT compatible general purpose computer.