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dc.contributor.authorMtetwa, Nhamoinesuen_UK
dc.contributor.authorSmith, Leslieen_UK
dc.description.abstractPrecision 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.en_UK
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_UK
dc.relationMtetwa N & Smith L (2005) Precision constrained stochastic resonance in a feedforward neural network. IEEE Transactions on Neural Networks, 16 (1), pp. 250-262.
dc.rightsThe 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.en_UK
dc.subjectStochastic resonanceen_UK
dc.subjectNeural networken_UK
dc.subjectLeaky integrate-and-fire neuronen_UK
dc.subjectStochastic processesen_UK
dc.subjectSignal processingen_UK
dc.subjectNeural networks (Computer science)en_UK
dc.titlePrecision constrained stochastic resonance in a feedforward neural networken_UK
dc.typeJournal Articleen_UK
dc.rights.embargoreason[IEEETransNNJan2005.pdf] The publisher does not allow this work to be made publicly available in this Repository therefore there is an embargo on the full text of the work.en_UK
dc.citation.jtitleIEEE Transactions on Neural Networksen_UK
dc.type.statusAM - Accepted Manuscripten_UK
dc.contributor.affiliationUniversity of Stirlingen_UK
dc.contributor.affiliationComputing Scienceen_UK
rioxxterms.typeJournal Article/Reviewen_UK
local.rioxx.authorMtetwa, Nhamoinesu|en_UK
local.rioxx.authorSmith, Leslie|0000-0002-3716-8013en_UK
local.rioxx.projectInternal Project|University of Stirling|
Appears in Collections:Computing Science and Mathematics Journal Articles

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