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

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.

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Correspondence to Brian C. Watson.

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Watson, B.C., Shoop, B.L., Ressler, E.K. et al. Analog-to-Digital Conversion Using Single-Layer Integrate-and-Fire Networks with Inhibitory Connections. EURASIP J. Adv. Signal Process. 2004, 894284 (2004). https://doi.org/10.1155/S1110865704405083

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Keywords and phrases

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