Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/90605
Citations
Scopus Web of Science® Altmetric
?
?
Type: Journal article
Title: Optimal sensor selection for noisy binary detection in stochastic pooling networks
Author: McDonnell, M.
Li, F.
Amblard, P.
Grant, A.
Citation: Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 2013; 88(2):022118-1-022118-9
Publisher: American Physical Society
Issue Date: 2013
ISSN: 1539-3755
1550-2376
Statement of
Responsibility: 
Mark D. McDonnell, Feng Li, P.-O. Amblard, and Alex J. Grant
Abstract: Stochastic Pooling Networks (SPNs) are a useful model for understanding and explaining how naturally occurring encoding of stochastic processes can occur in sensor systems ranging from macroscopic social networks to neuron populations and nanoscale electronics. Due to the interaction of nonlinearity, random noise, and redundancy, SPNs support various unexpected emergent features, such as suprathreshold stochastic resonance, but most existing mathematical results are restricted to the simplest case where all sensors in a network are identical. Nevertheless, numerical results on information transmission have shown that in the presence of independent noise, the optimal configuration of a SPN is such that there should be partial heterogeneity in sensor parameters, such that the optimal solution includes clusters of identical sensors, where each cluster has different parameter values. In this paper, we consider a SPN model of a binary hypothesis detection task and show mathematically that the optimal solution for a specific bound on detection performance is also given by clustered heterogeneity, such that measurements made by sensors with identical parameters either should all be excluded from the detection decision or all included. We also derive an algorithm for numerically finding the optimal solution and illustrate its utility with several examples, including a model of parallel sensory neurons with Poisson firing characteristics.
Rights: ©2013 American Physical Society
RMID: 0030013334
DOI: 10.1103/PhysRevE.88.022118
Grant ID: http://purl.org/au-research/grants/arc/DP1093425
Appears in Collections:Electrical and Electronic Engineering publications

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.