Infection-driven gut dysbiosis and epigenetic programming of microglia: toward a systems level framework linking microbial metabolites, neuroinflammation, synaptic dysfunction, and probiotic modulatio

Music is a universal language that transcends cultural and linguistic barriers, playing a vital role in human cognition and emotional expression. Understanding how musical sequences are encoded cognitively is not only essential for advancing our knowledge of human brain function but also has practical implications for applications such as music generation (), recommendation systems, and therapeutic interventions. The study of cognitive encoding of musical sequences enables researchers to explore the intricate interplay between auditory perception, memory, and neural processing (). Not only does this research contribute to the broader field of cognitive science, but it also provides insights into the development of computational models that mimic human-like understanding of music (). Furthermore, modeling musical cognition through learning-based neural frameworks can enhance the design of intelligent systems capable of generating and interpreting music in a manner that aligns with human preferences and emotions (). This task is particularly significant in the era of artificial intelligence, where bridging the gap between human cognition and machine learning remains a critical challenge (). The notion of cognitive encoding in this study is used in a computational and cognition-inspired sense rather than as a claim of direct neural reconstruction (). Evidence from music cognition and auditory neuroscience suggests that musical stimuli with different acoustic, rhythmic, harmonic, or tonal properties can elicit systematically different neural and behavioral responses (). Such findings indicate that brain responses are sensitive to structured musical information, but they do not by themselves imply that the original musical stimulus can be fully reconstructed from brain activity or that there exists a one-to-one mapping between neural activity and musical sequences (). In this paper, we therefore use these studies as motivation for learning structured representations of musical sequences, not as evidence that the proposed model reproduces biological encoding mechanisms (). Prior work on auditory and music perception has shown that cortical and electrophysiological responses reflect temporal, spectral, tonal, and expectation-related properties of musical stimuli (). These observations support the view that musical sequence modeling should account for temporal organization, structural regularities, and response variability. The proposed framework focuses on three computational aspects that are relevant to cognition-inspired modeling: structured representation learning, temporal dependency modeling, and uncertainty-aware refinement (). In the early stages of research, efforts to model the cognitive encoding of musical sequences primarily focused on structured frameworks that utilized predefined rules and expert knowledge. These methods aimed to capture the structural and hierarchical aspects of musical cognition by representing music through elements such as pitch, rhythm, and harmony (). While these approaches provided a foundational understanding, they were often limited by their inability to adapt to the variability and complexity of human musical perception (). The rigid nature of these frameworks restricted their effectiveness in capturing the nuances of human cognition, as they struggled to generalize across diverse musical styles and contexts (). This prompted researchers to explore more flexible methodologies that could learn directly from musical data, paving the way for the integration of data-driven techniques. The introduction of machine learning techniques brought a paradigm shift in the study of musical cognition, allowing for the modeling of musical sequences through statistical and probabilistic methods. By utilizing large datasets of musical compositions and performances, algorithms were developed to identify patterns and predict musical structures without relying on predefined rules (). These methods offered improved adaptability and flexibility compared to earlier approaches, as they could learn directly from data (). However, the reliance on feature engineering posed challenges, as it required significant domain expertise to extract meaningful representations of musical sequences (). These methods often faced challenges in capturing the hierarchical and temporal dependencies inherent in music, which limited their capacity to model complex cognitive processes (). This led to the exploration of deep learning techniques, which provided a more robust solution for modeling musical cognition.