BAN: Neuroanatomical Aligning in Auditory Recognition between Artificial Neural Network and Human Cortex

2025-02-21

AI Summary

Conventional artificial neural networks (ANNs) achieve high performance in auditory recognition but lack biological plausibility—particularly due to excessive depth and absence of recurrent connectivity—hindering alignment with the human auditory pathway. Method: We propose the Brain-inspired Auditory Network (BAN), the first ANN architecture explicitly mapped to the neuroanatomical structure of human temporal lobe auditory cortices (T2/T3), incorporating recurrent connections to enhance biological fidelity. We introduce the Brain-like Auditory Score (BAS), a novel cross-species metric quantifying functional similarity between artificial and biological auditory systems. Contribution/Results: Validated through neuroanatomical modeling, multi-scale functional alignment assessment, and music genre classification, BAN significantly outperforms deep ANNs in both recognition accuracy and BAS. Results demonstrate a strong correlation between auditory recognition capability and structural similarity to cortical auditory organization, establishing neuroanatomical alignment as a key design principle for biologically grounded auditory AI.

Technology Category

• Humans and AI: Brain-Sensing and Analysis
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Bio-inspired Learning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Drawing inspiration from neurosciences, artificial neural networks (ANNs) have evolved from shallow architectures to highly complex, deep structures, yielding exceptional performance in auditory recognition tasks. However, traditional ANNs often struggle to align with brain regions due to their excessive depth and lack of biologically realistic features, like recurrent connection. To address this, a brain-like auditory network (BAN) is introduced, which incorporates four neuroanatomically mapped areas and recurrent connection, guided by a novel metric called the brain-like auditory score (BAS). BAS serves as a benchmark for evaluating the similarity between BAN and human auditory recognition pathway. We further propose that specific areas in the cerebral cortex, mainly the middle and medial superior temporal (T2/T3) areas, correspond to the designed network structure, drawing parallels with the brain's auditory perception pathway. Our findings suggest that the neuroanatomical similarity in the cortex and auditory classification abilities of the ANN are well-aligned. In addition to delivering excellent performance on a music genre classification task, the BAN demonstrates a high BAS score. In conclusion, this study presents BAN as a recurrent, brain-inspired ANN, representing the first model that mirrors the cortical pathway of auditory recognition.

Problem

• Aligning ANN with human auditory cortex
• Incorporating neuroanatomical features into ANN
• Improving auditory recognition using brain-like structures

Innovation

• Recurrent neural connections
• Neuroanatomically mapped areas
• Brain-like auditory score