Neural network models of tinnitus.
In this chapter we review the relatively recent effort on the part of neuroscientists to use computational neural network modeling to investigate the neural basis of subjective tinnitus. There are advantages and challenges in using a modeling framework to understand this complex auditory disorder. The foremost challenge to modeling a subjective condition such as tinnitus is the evaluation of the occurrence of tinnitus in the model. We propose comparing measures of the model's activities (simulated neuronal activity, behavioral activity, or neuroimaging activity) with experimental data obtained from studies of tinnitus in humans and animals; strong agreement with experimental data will provide support for the validity of the simulation of tinnitus in a particular model. A major advantage of neural network modeling is that it allows experimentation not possible in animals. Models make it possible to evaluate the contribution of different neural mechanisms affecting tinnitus in a principled manner. A model makes predictions that can be tested by experiments thus leading to the designing of focused experiments. We review several neural models of tinnitus and discuss published findings from simulations using these models. We conclude with a proposed scheme for investigating tinnitus that combines neural network modeling with brain imaging experiments.
Husain
Fatima T
FT
Brain Imaging and Modeling Section, National Institute on Deafness and Other Communication Disorders, NIH, Bethesda, MD 20892, USA. husainf@nidcd.nih.gov
D000818:Animals; D001303:Auditory Cortex / Q000503:physiopathology*; D006801:Humans; D008279:Magnetic Resonance Imaging; D016571:Neural Networks, Computer; D013788:Thalamus / Q000503:physiopathology*; D014012:Tinnitus / Q000503:physiopathology*