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Citation

Mtetwa N & Smith L (2005) Precision constrained stochastic resonance in a feedforward neural network. IEEE Transactions on Neural Networks, 16 (1), pp. 250-262. https://doi.org/10.1109/TNN.2004.836195

Abstract
Precision constrained stochastic resonance in a feedforward neural network Mtetwa, N. Smith, L.S. Dept. of Comput. Sci., Univ. of Stirling, UK; This paper appears in: Neural Networks, IEEE Transactions on Publication Date: Jan. 2005 Volume: 16, Issue: 1 On page(s): 250-262 ISSN: 1045-9227 INSPEC Accession Number: 8278373 Digital Object Identifier: 10.1109/TNN.2004.836195 Current Version Published: 2005-01-31 Abstract Stochastic resonance (SR) is a phenomenon in which the response of a nonlinear system to a subthreshold information-bearing signal is optimized by the presence of noise. By considering a nonlinear system (network of leaky integrate-and-fire (LIF) neurons) that captures the functional dynamics of neuronal firing, we demonstrate that sensory neurons could, in principle harness SR to optimize the detection and transmission of weak stimuli. We have previously characterized this effect by use of signal-to-noise ratio (SNR). Here in addition to SNR, we apply an entropy-based measure (Fisher information) and compare the two measures of quantifying SR. We also discuss the performance of these two SR measures in a full precision floating point model simulated in Java and in a precision limited integer model simulated on a field programmable gate array (FPGA). We report in this study that stochastic resonance which is mainly associated with floating point implementations is possible in both a single LIF neuron and a network of LIF neurons implemented on lower resolution integer based digital hardware. We also report that such a network can improve the SNR and Fisher information of the output over a single LIF neuron.

Keywords
Stochastic resonance; Neural network; Leaky integrate-and-fire neuron; Stochastic processes; Signal processing; Neural networks (Computer science)

Journal
IEEE Transactions on Neural Networks: Volume 16, Issue 1