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

Received: 14 December 2003

Published: 5 October 2004


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

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


© Watson et al. 2004