Krauss & Schilling's stochastic resonance model reframes tinnitus as adaptive optimization

The stochastic resonance (SR) model of tinnitus, introduced in 2016 by Patrick Krauss and Achim Schilling, proposes that subjective tinnitus—the perception of sound without an external source—arises from an adaptive upregulation of neural noise in the auditory system after hearing loss. Rather than viewing increased spontaneous activity as pathological, the SR model interprets it as a functional mechanism that enhances signal detection near sensory thresholds. Over the past decade, this framework has evolved from a phenomenological hypothesis into a broader neurocomputational theory, integrating information theory, adaptive signal detection, multichannel auditory processing, and cross-modal plasticity. Converging evidence from computational models, large-scale clinical studies, and animal experiments supports key predictions, including improved detectability under specific noise conditions and the generation of frequency-specific phantom percepts.

The SR model has also inspired a novel therapeutic strategy: spectrally matched near-threshold noise stimulation, which aims to artificially reintroduce noise to shift the brain away from its internally generated adaptive state. More recently, the framework has been unified with central gain, homeostatic plasticity, and predictive coding to provide a comprehensive account of auditory phantom perception. This review, published on arXiv in June 2026, provides a chronological overview of the model's development, summarizes major theoretical and empirical advances, and outlines future directions for mechanistic validation and clinical translation. By redefining tinnitus as a consequence of adaptive sensory computation, the model shifts the conceptual focus from pathological dysfunction toward principles of information optimization in neural systems, offering a paradigm shift in how we understand and treat chronic tinnitus.