title: Stochastic resonance with colored noise for neural signal detection.
abstract: We 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.
authors.affiliation: Institute of Complexity Science, Qingdao University, Qingdao, P. R. China.
authors.forename: Fabing
authors.initials: F
authors.lastname: Duan
authors.affiliation: Laboratoire d'Ingénierie des Systèmes Automatisés, Université d'Angers, Angers, France.
authors.forename: François
authors.initials: F
authors.lastname: Chapeau-Blondeau
authors.affiliation: Centre for Biomedical Engineering and School of Electrical & Electronic Engineering, The University of Adelaide, Adelaide, Southern Australia, Australia.
authors.forename: Derek
authors.initials: D
authors.lastname: Abbott
mesh_terms: D000465:Algorithms; D008962:Models, Theoretical; D009474:Neurons / Q000502:physiology*; D009622:Noise; D017603:Signal Detection, Psychological; D013269:Stochastic Processes