title: Stochastic resonance tuned by correlations in neural models.
abstract: The idea that neurons might use stochastic resonance (SR) to take advantage of random signals has been extensively discussed in the literature. However, there are a few key issues that have not been clarified and thus it is difficult to assess that whether SR in neuronal models occurs inside plausible physiology parameter regions or not. We propose and show that neurons can adjust correlations between synaptic inputs, which can be measured in experiments and are dynamical variables, to exhibit SR. The benefit of such a mechanism over the conventional SR is also discussed.
authors.affiliation: Computational Neuroscience Laboratory, The Babraham Institute, Cambridge CB2 4AT, United Kingdom. jf218@cam.ac.uk
authors.forename: J
authors.initials: J
authors.lastname: Feng
authors.forename: B
authors.initials: B
authors.lastname: Tirozzi
mesh_terms: D000818:Animals; D055592:Biophysical Phenomena; D001703:Biophysics; D019706:Excitatory Postsynaptic Potentials / Q000502:physiology; D006801:Humans; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D015398:Signal Transduction; D013269:Stochastic Processes; D013569:Synapses / Q000502:physiology