Predictive Coding and Stochastic Resonance: Towards a Unified Theory of Auditory (Phantom) Perception

• URL: http://arxiv.org/abs/2204.03354v1
• Date: Thu, 7 Apr 2022 10:47:58 GMT
• Title: Predictive Coding and Stochastic Resonance: Towards a Unified Theory of Auditory (Phantom) Perception
• Authors: Achim Schilling, William Sedley, Richard Gerum, Claus Metzner, Konstantin Tziridis, Andreas Maier, Holger Schulze, Fan-Gang Zeng, Karl J. Friston, Patrick Krauss
• Abstract summary: To gain a mechanistic understanding of brain function, hypothesis driven experiments should be accompanied by biologically plausible computational models. With a special focus on tinnitus, we review recent work at the intersection of artificial intelligence, psychology, and neuroscience. We conclude that two fundamental processing principles - being ubiquitous in the brain - best fit to a vast number of experimental results.
• Score: 6.416574036611064
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cognitive computational neuroscience (CCN) suggests that to gain a mechanistic understanding of brain function, hypothesis driven experiments should be accompanied by biologically plausible computational models. This novel research paradigm offers a way from alchemy to chemistry, in auditory neuroscience. 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, foregrounding the idea that experiments will yield mechanistic insight only when employed to test formal or computational models. This view challenges the popular notion that tinnitus research is primarily data limited, and that producing large, multi-modal, and complex data-sets, analyzed with advanced data analysis algorithms, will lead to fundamental insights into how tinnitus emerges. We conclude that two fundamental processing principles - being ubiquitous in the brain - best fit to a vast number of experimental results and therefore provide the most explanatory power: predictive coding as a top-down, and stochastic resonance as a complementary bottom-up mechanism. Furthermore, we argue that even though contemporary artificial intelligence and machine learning approaches largely lack biological plausibility, the models to be constructed will have to draw on concepts from these fields; since they provide a formal account of the requisite computations that underlie brain function. Nevertheless, biological fidelity will have to be addressed, allowing for testing possible treatment strategies in silico, before application in animal or patient studies. This iteration of computational and empirical studies may help to open the "black boxes" of both machine learning and the human brain.

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