Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/1698

Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Precision constrained stochastic resonance in a feedforward neural network
Authors: Mtetwa, Nhamoinesu
Smith, Leslie
Contact Email: l.s.smith@cs.stir.ac.uk
Keywords: Stochastic resonance
Neural network
Leaky integrate-and-fire neuron
Issue Date: Jan-2005
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
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.
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.
Type: Journal Article
URI: http://hdl.handle.net/1893/1698
DOI Link: http://dx.doi.org/10.1109/TNN.2004.836195
Rights: The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author; you can only request a copy if you wish to use this work for your own research or private study.
Affiliation: University of Stirling
Computing Science - CSM Dept

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