# Predictive coding and stochastic resonance as fundamental principles of auditory perception

arXiv: https://arxiv.org/abs/2204.03354 · HTML: https://mlsanity.com/paper/2204.03354/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 168 references that are in the map.
- First posted on arXiv 7 Apr 2022.

## Builds on (most cited first, 13 of 13)

- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Towards an integration of deep learning and neuroscience](https://mlsanity.com/paper/1606.03813/) (2016)
- [Cognitive computational neuroscience](https://mlsanity.com/paper/1807.11819/) (2018)
- [How deep is deep enough? -- Quantifying class separability in the hidden layers of deep neural networks](https://mlsanity.com/paper/1811.01753/) (2018)
- [A statistical method for analyzing and comparing spatiotemporal cortical activation patterns](https://mlsanity.com/paper/1611.07677/) (2016)
- [Sparsity through evolutionary pruning prevents neuronal networks from overfitting](https://mlsanity.com/paper/1911.10988/) (2019)
- [When Noise meets Chaos: Stochastic Resonance in Neurochaos Learning](https://mlsanity.com/paper/2102.01316/) (2021)
- [Integration of Leaky-Integrate-and-Fire-Neurons in Deep Learning Architectures](https://mlsanity.com/paper/2004.13532/) (2020)
- [Neural dynamics under active inference: plausibility and efficiency of information processing](https://mlsanity.com/paper/2001.08028/) (2020)
- [Stochastic resonance controlled upregulation of internal noise after hearing loss as a putative correlate of tinnitus-related neuronal hyperactivity](https://mlsanity.com/paper/1603.04721/) (2016)
- [Dynamical Phases and Resonance Phenomena in Information-Processing Recurrent Neural Networks](https://mlsanity.com/paper/2108.02545/) (2021)
- [The True Title of Bayes's Essay](https://mlsanity.com/paper/1310.0173/) (2013)
- [Open(G)PIAS: An open source solution for the construction of a high-precision Acoustic-Startle-Response (ASR) setup for tinnitus screening and threshold estimation in rodents](https://mlsanity.com/paper/1804.09667/) (2018)

## Abstract (arXiv)

How is information processed in the brain during perception? Mechanistic insight is achieved only when experiments are employed to test formal or computational models. In analogy to lesion studies, phantom perception may serve as a vehicle to understand the fundamental processing principles underlying auditory perception. With a special focus on tinnitus -- as the prime example of auditory phantom perception -- we review recent work at the intersection of artificial intelligence, psychology, and neuroscience. In particular, we discuss why everyone with tinnitus suffers from hearing loss, but not everyone with hearing loss suffers from tinnitus. We argue that the increase of sensory precision due to Bayesian inference could be caused by intrinsic neural noise and lead to a prediction error in the cerebral cortex. Hence, two fundamental processing principles - being ubiquitous in the brain - provide the most explanatory power for the emergence of tinnitus: predictive coding as a top-down, and stochastic resonance as a complementary bottom-up mechanism. We conclude that both principles play a crucial role in healthy auditory perception.