  This paper presents a novel approximation unit added to the conventional
spike processing chain which provides an appreciable reduction of complexity of
the high-hardware cost feature extractors. The use of the Taylor polynomial is
proposed and modelled employing its cascaded derivatives to non-uniformly
capture the essential samples in each spike for reliable feature extraction and
sorting. Inclusion of the approximation unit can provide 3X compression (i.e.
from 66 to 22 samples) to the spike waveforms while preserving their shapes.
Detailed spike waveform sequences based on in-vivo measurements have been
generated using a customized neural simulator for performance assessment of the
approximation unit tested on six published feature extractors. For noise levels
{\sigma}_N between 0.05 and 0.3 and groups of 3 spikes in each channel, all the
feature extractors provide almost same sorting performance before and after
approximation. The overall implementation cost when including the approximation
unit and feature extraction shows a large reduction (i.e. up to 8.7X) in the
hardware costly and more accurate feature extractors, offering a substantial
improvement in feature extraction design.
