title: Error-probability noise benefits in threshold neural signal detection.
abstract: Five new theorems and a stochastic learning algorithm show that noise can benefit threshold neural signal detection by reducing the probability of detection error. The first theorem gives a necessary and sufficient condition for such a noise benefit when a threshold neuron performs discrete binary signal detection in the presence of additive scale-family noise. The theorem allows the user to find the optimal noise probability density for several closed-form noise types that include generalized Gaussian noise. The second theorem gives a noise-benefit condition for more general threshold signal detection when the signals have continuous probability densities. The third and fourth theorems reduce this noise benefit to a weighted-derivative comparison of signal probability densities at the detection threshold when the signal densities are continuously differentiable and when the noise is symmetric and comes from a scale family. The fifth theorem shows how collective noise benefits can occur in a parallel array of threshold neurons even when an individual threshold neuron does not itself produce a noise benefit. The stochastic gradient-ascent learning algorithm can find the optimal noise value for noise probability densities that do not have a closed form.
authors.affiliation: Department of Electrical Engineering, Signal and Image Processing Institute, University of Southern California, Los Angeles, CA 90089-2564, USA.
authors.forename: Ashok
authors.initials: A
authors.lastname: Patel
authors.forename: Bart
authors.initials: B
authors.lastname: Kosko
mesh_terms: D000465:Algorithms; D007858:Learning / Q000502:physiology; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D016011:Normal Distribution; D011336:Probability; D017603:Signal Detection, Psychological / Q000502:physiology*; D013269:Stochastic Processes