Evaluation of logarithmic vs. linear ADCs for neural signal acquisition and reconstruction.

Abstract

An evaluation of the effectiveness of logarithmic quantization for neural signals is performed in this paper. Logarithmic analog to digital converters (ADCs) are employed in biomedical applications where signals with high dynamic range are recorded. For the same number of bits of a linear ADC, a logarithmic one can better resolve smaller signals, at a price of worse accuracy for high amplitudes. This feature can also reduce the number of bits required and then allow data reduction as well. No study was done to verify the efficacy of such ADCs on neural signals in the context of spike sorting. Using simulated and recorded publically available data this is done extensively in the paper. Neural signals are quantized with linear and logarithmic ADCs. Then using the original signal as reference, the new signals are processed with Osort for automated spike sorting. The results are compared with the reference to determine whether one of the two quantization methods provides some benefits. The result is that logarithmic ADCs outperform linear quantization only in the range from 2 to 5 bits. Such low resolutions are unfortunately not enough for proper spike sorting, hence logarithmic ADCs appear not to provide an improvement over a conventional ADC.