Hearing Music: A Shared Geometry Governs the Trade-off Between Reliability and Complexity in the Neural Code
Pauline G. Mouawad∗1,Shievanie Sabesan1, Alinka E. Greasley2, Nicholas A. Lesica1 1The Ear Institute, University College London, London, UK 2School of Music, University of Leeds, Leeds, UK
*Email: p.mouawad@ucl.ac.uk
Introduction Music is central to human culture, shaping social bonds and emotional well-being. Its unique ability to connect sensory processing with reward, emotion, and statistical learning makes it an ideal tool for studying auditory perception [1]. Previous studies have explored neural responses to speech and to simple musical sounds [2, 3], but the neural coding of complex music remains unexplored. We addressed this gap by analyzing multi-unit activity (MUA) recorded from the inferior colliculus (IC) of normal-hearing (NH) and hearing-impaired (HI) gerbils in response to a range of music types at multiple sound levels. The music types included individual stems (vocals, drums, bass, and other) as well as mixtures in which the stems were combined. Methods Using coherence analysis, we assessed how reliably music is encoded in the IC across repeated presentations of stimuli and the degree to which individual stems are distorted when presented in a mixture. To explore neural activity patterns at the network level, we implemented a manifold analysis using PCA. This identified the signal manifold, the subspace where reliable musical information is embedded. To model neural transformations underlying music encoding, we developed a deep neural network (DNN) capable of generating MUA from sound, providing a framework for interpreting how the IC processes music. Finally, to assess the impact of hearing loss, we conducted a comparative analysis for NH and HI at equal sound and sensation levels. Results We identified strong nonlinear interactions between stems, affecting both the reliability and geometry of neural coding. The reliability of the responses and the dimensionality of the signal manifold varied widely across music types. With increasing musical complexity, the dimensionality of the signal manifold increased, however the reliability decreased. The leading modes in the signal manifold were reliable and shared across all music types, but as musical complexity increased, new neural modes emerged, though these were increasingly unreliable (Figure 1). Our DNN successfully synthesized MUA from music with high fidelity. After hearing loss, neural coding was strongly distorted at equal sound level, but these distortions were largely corrected at equal sensation level. Discussion
Music processing in the early auditory pathway involves nonlinear interactions that shape the neural representation in complex ways. The signal manifold contains a fixed set of leading modes that are invariant across music types. As music becomes more complex the manifold is not reconfigured; instead, new, less reliable modes are added. These new modes reflect a fundamental trade-off between fidelity and complexity in the neural code. The fact that suitable amplification restores near-normal neural coding suggests that mild-to-moderate hearing loss primarily affects audibility rather than the brainstem’s capacity to process music. Figure 1. Complexity and Reliability in the Latent Space Acknowledgements Funding for this work was provided by the UK Medical Research Council through grant MR/W019787/1. References 1. Patrik N Juslin and Daniel Västfjäll. “Emotional responses to music: The need to consider underlying mechanisms”.https://doi.org/10.1017/S0140525X08005293. 2. Vani G Rajendran et al. “Midbrain adaptation may set the stage for the perception of musical beat”. In: Proceedings of the Royal Society B: Biological Sciences 284.1866 (2017), p. 20171455. https://doi.org/10.1098/rspb.2017.1455. 3. Shievanie Sabesan et al. “Large-scale electrophysiology and deep learning reveal distorted neural signal dynamics after hearing loss”. In: Elife 12 (2023), e85108. https://doi.org/10.7554/eLife.85108.
