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Energy-Efficient Digital Processing for Neural Action Potentials

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Neural Computation, Neural Devices, and Neural Prosthesis

Abstract

This chapter discusses algorithm, architecture, and circuit techniques for efficient implementation of neural signal processing circuits. In particular, the focus is on spike sorting and compressive sampling for action potentials. The chapter begins with an introduction to spike sorting and compressive sampling, and the need for their implementation in modern-day neural recording systems. We then illustrate, through examples, some useful methods for algorithm selection and optimization. Digital design techniques that are beneficial in power and area reduction for neural signal processing DSPs are also discussed. Finally, we discuss the challenges and future directions in the area of biosignal processing.

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Notes

  1. 1.

    Local field potentials (LFPs) are of interest to clinicians for several studies like diagnosis of epileptic patients. However, the focus of this chapter is spike sorting. Hence, we assume LFP to be filtered immediately after the signal is recorded.

  2. 2.

    The data-rate reduction numbers correspond to a 64-channel system with a sampling rate of 24 kSa/s. The typical action potential spans 48 samples and the spike firing rate is assumed to be 100 spikes/second.

  3. 3.

    We refer to the process of applying random linear projections to a signal after sampling at the Nyquist rate as soft(-ware) CS to distinguish it from hard(-ware) CS, where the projections or their equivalent are performed in the analog or physical domain before sampling or digitization.

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Authors and Affiliations

  1. Department of Electrical Engineering, University of California, Los Angeles, CA, USA

    Vaibhav Karkare, Sarah Gibson & Dejan Marković

Authors
  1. Vaibhav Karkare
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  2. Sarah Gibson
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  3. Dejan Marković
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Corresponding author

Correspondence to Dejan Marković .

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Editors and Affiliations

  1. National University of Singapore, Singapore, Singapore

    Zhi Yang

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Karkare, V., Gibson, S., Marković, D. (2014). Energy-Efficient Digital Processing for Neural Action Potentials. In: Yang, Z. (eds) Neural Computation, Neural Devices, and Neural Prosthesis. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8151-5_2

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