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Proceedings Paper

Pooling networks for a discrimination task: noise-enhanced detection
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Paper Abstract

Pooling networks are composed of noisy independent neurons that all noisily process the same information in parallel. The output of each neuron is summed into a single output by a fusion center. In this paper we study such a network in a detection or discrimination task. It is shown that if the network is not properly matched to the symmetries of the detection problem, the internal noise may restore at least partially some kind of optimality. This is shown for both (i) noisy threshold model neurons, as well as (ii) Poisson neuron models. We also study an optimized version of the network, mimicking the notion of excitation/inhibition. We show that, when properly tuned, the network may reach optimality in a very robust way. Furthermore, we find in this optimization that some neurons remain inactive. The pattern of inactivity is organized in a strange branching structure, the meaning of which remains to be elucidated.

Paper Details

Date Published: 15 June 2007
PDF: 12 pages
Proc. SPIE 6602, Noise and Fluctuations in Biological, Biophysical, and Biomedical Systems, 66020S (15 June 2007); doi: 10.1117/12.724641
Show Author Affiliations
Pierre-Olivier Amblard, GIPSA-lab, CNRS (France)
Steeve Zozor, GIPSA-lab, CNRS (France)
Mark D. McDonnell, The Univ. of Adelaide (Australia)
Nigel G. Stocks, The Univ. of Warwick (United Kingdom)

Published in SPIE Proceedings Vol. 6602:
Noise and Fluctuations in Biological, Biophysical, and Biomedical Systems
Sergey M. Bezrukov, Editor(s)

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