Duan, F.Chapeau-Blondeau, F.Abbott, D.Chacron, M.J.2014-08-202014-08-202014PLoS ONE, 2014; 9(3):e91345-1932-62031932-6203http://hdl.handle.net/2440/84477We analyze signal detection with nonlinear test statistics in the presence of colored noise. In the limits of small signal and weak noise correlation, the optimal test statistic and its performance are derived under general conditions, especially concerning the type of noise. We also analyze, for a threshold nonlinearity-a key component of a neural model, the conditions for noise-enhanced performance, establishing that colored noise is superior to white noise for detection. For a parallel array of nonlinear elements, approximating neurons, we demonstrate even broader conditions allowing noise-enhanced detection, via a form of suprathreshold stochastic resonance.en© 2014 Duan et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.NeuronsStochastic ProcessesNoiseModels, TheoreticalSignal Detection, PsychologicalAlgorithmsJournal article003000683310.1371/journal.pone.00913450003328584000562-s2.0-8489845879872112Abbott, D. [0000-0002-0945-2674]