Analog-to-digital conversion using single-layer integrate-and-fire networks with inhibitory connections

  • Authors:

  • Brian C. Watson;Barry L. Shoop;Eugene K. Ressler;Pankaj K. Das

  • Affiliations:

  • Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA;Department of Electrical Engineering and Computer Science, Photonics Research Center, United States Military Academy, West Point, NY;Department of Electrical Engineering and Computer Science, Photonics Research Center, United States Military Academy, West Point, NY;Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA

  • Venue:
  • EURASIP Journal on Applied Signal Processing
  • Year:
  • 2004

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Abstract

We discuss amethod for increasing the effective sampling rate of binary A/D converters using an architecture that is inspired by biological neural networks. As in biological systems, many relatively simple components can act in concert without a predetermined progression of states or even a timing signal (clock). The charge-fire cycles of individual A/D converters are coordinated using feedback in a manner that suppresses noise in the signal baseband of the power spectrum of output spikes. We have demonstrated that these networks self-organize and that by utilizing the emergent properties of such networks, it is possible to leverage many A/D converters to increase the overall network sampling rate. We present experimental and simulation results for networks of oversampling 1-bit A/D converters arranged in single-layer integrate-and-fire networks with inhibitory connections. In addition, we demonstrate information transmission and preservation through chains of cascaded single-layer networks.