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In order to gain a mechanistic understanding of how tinnitus emerges in thebrain, we must build biologically plausible computational models that mimicboth tinnitus development and perception, and test the tentative models withbrain and behavioral experiments. With a special focus on tinnitus research, wereview recent work at the intersection of artificial intelligence, psychologyand neuroscience, indicating a new research agenda that follows the idea thatexperiments will yield theoretical insight only when employed to testbrain-computational models. This view challenges the popular belief, thattinnitus research is primarily data limited, and that producing large,multi-modal, and complex datasets, analyzed with advanced data analysisalgorithms, will finally lead to fundamental insights into how tinnitusemerges. However, there is converging evidence that although moderntechnologies allow assessing neural activity in unprecedentedly rich ways inboth, animals and humans, empirical testing one verbally defined hypothesisabout tinnitus after another, will never lead to a mechanistic understanding.Instead, hypothesis testing needs to be complemented with the construction ofcomputational models that generate verifiable predictions. We argue, that eventhough, contemporary artificial intelligence and machine learning approacheslargely lack biological plausibility, the models to be constructed will have todraw on concepts from these fields, since they have already proven to do wellin modeling brain function. Nevertheless, biological fidelity will have to beincreased successively, leading to ever better and fine-grained models,allowing at the end for even testing possible treatment strategies in silico,before application in animal or patient studies.

Towards a Cognitive Computational Neuroscience of Auditory Phantom Perceptions