Open Access

Analog-to-Digital Conversion Using Single-Layer Integrate-and-Fire Networks with Inhibitory Connections

  • Brian C. Watson1Email author,
  • Barry L. Shoop2,
  • Eugene K. Ressler2 and
  • Pankaj K. Das1
EURASIP Journal on Advances in Signal Processing20042004:894284

DOI: 10.1155/S1110865704405083

Received: 14 December 2003

Published: 5 October 2004

Abstract

We discuss a method 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.

Keywords and phrases

spiking neurons analog-to-digital conversion integrate-and-fire networks neuroscience

Authors’ Affiliations

(1)
Department of Electrical and Computer Engineering, University of California
(2)
Department of Electrical Engineering and Computer Science, Photonics Research Center, United States Military Academy

Copyright

© Watson et al. 2004