title: The mechanism for stochastic resonance enhancement of mammalian auditory information processing.
abstract: BACKGROUND
  In a mammalian auditory system, when intrinsic noise is added to a subthreshold signal, not only can the resulting noisy signal be detected, but also the information carried by the signal can be completely recovered. Such a phenomenon is called stochastic resonance (SR). Current analysis of SR commonly employs the energies of the subthreshold signal and intrinsic noise. However, it is difficult to explain SR when the energy addition of the signal and noise is not enough to lift the subthreshold signal over the threshold. Therefore, information modulation has been hypothesized to play a role in some forms of SR in sensory systems. Information modulation, however, seems an unlikely mechanism for mammalian audition, since it requires significant a priori knowledge of the characteristics of the signal.
  
  
  RESULTS
  We propose that the analysis of SR cannot rely solely on the energies of a subthreshold signal and intrinsic noise or on information modulation. We note that a mammalian auditory system expends energy in the processing of a noisy signal. A part of the expended energy may therefore deposit into the recovered signal, lifting it over threshold. We propose a model that in a rigorous mathematical manner expresses this new theoretical viewpoint on SR in the mammalian auditory system and provide a physiological rationale for the model.
  
  
  CONCLUSIONS
  Our result indicates that the mammalian auditory system may be more active than previously described in the literature. As previously recognized, when intrinsic noise is used to generate a noisy signal, the energy carried by the noise is added to the original subthreshold signal. Furthermore, our model predicts that the system itself should deposit additional energy into the recovered signal. The additional energy is used in the processing of the noisy signal to recover the original subthreshold signal.
authors.affiliation: Department of Computer Science, Rutgers University, Camden, New Jersey, USA. dhong@camden.rutgers.edu <dhong@camden.rutgers.edu>
authors.forename: Dawei
authors.initials: D
authors.lastname: Hong
authors.forename: Joseph V
authors.initials: JV
authors.lastname: Martin
authors.forename: William M
authors.initials: WM
authors.lastname: Saidel
mesh_terms: D000465:Algorithms; D000818:Animals; D001307:Auditory Perception / Q000502:physiology*; D003198:Computer Simulation; D006309:Hearing; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D008962:Models, Theoretical; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated; D012684:Sensory Thresholds; D013269:Stochastic Processes