29 March 2004 Neural mechanisms for analog-to-digital conversion
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Abstract
Consider an array of threshold devices, such as neurons or comparators, where each device receives the same input signal, but is subject to independent additive noise. When the output from each device is summed to give an overall output, the system acts as a noisy Analog to Digital Converter (ADC). Recently, such a system was analyzed in terms of information theory, and it was shown that under certain conditions the transmitted information through the array is maximized for non-zero noise. Such a phenomenon where noise can be of benefit in a nonlinear system is termed Stochastic Resonance (SR). The effect in the array of threshold devices was termed Suprathreshold Stochastic Resonance (SSR) to distinguish it from conventional forms of SR, in which usually a signal needs to be subthreshold for the effect to occur. In this paper we investigate the efficiency of the analog to digital conversion when the system acts like an array of simple neurons, by analyzing the average distortion incurred and comparing this distortion to that of a conventional flash ADC.
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Mark D. McDonnell, Derek Abbott, Charles E.M. Pearce, "Neural mechanisms for analog-to-digital conversion", Proc. SPIE 5275, BioMEMS and Nanotechnology, (29 March 2004); doi: 10.1117/12.523165; https://doi.org/10.1117/12.523165
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