Effects of quantization on neural spike sorting

Wireless neural recording systems require the data acquisition and signal-processing hardware to be moved to the transmit side. The strict power-density contraints on implanted devices require new ideas for system power minimization. Minimizing the number of bits of the ADC would have a significant impact on the total system power by reducing the power of the ADC, the DSP, and the transmitter. In this paper we examine the effects of quantization on the performance of spike sorting. We derive the resolution required of uniform quantizers to ensure the most accurate spike detection and clustering, and compare this to simulation results. We then provide evidence that optimal quantizers are well suited for neural data, and show that optimal quantizers provide a savings of at least 2 bits compared to uniform quantizers.