用轻量卷积模型高效模拟听觉神经信号，适合实时应用。
An Efficient Neural Network for Modeling Human Auditory Neurograms for Speech
- 设计紧凑卷积编码器，直接映射音频到多频神经图谱。
- 相比传统模型计算量降低，且相同输入输出一致。
- 适合听觉神经科学与音频处理的前端建模使用。
经典听觉外围模型（如Bruce等，2018）虽具高保真度，但具有随机性且计算开销大，限制大规模实验和低延迟应用。先前的神经编码器虽能近似部分外围特征，但很少显式训练以复现确定性的率域神经图谱，阻碍了真实对比评估。本文提出一种轻量级卷积编码器，逼近Bruce均率路径，将音频映射为多频神经图谱。我们刻意忽略随机放电效应，专注于确定性映射（相同输入产生相同输出）。通过计算高效的架构设计，该编码器在保持与参考模型高度一致的同时显著降低计算成本，实现了高效建模，适用于听觉神经科学与音频信号处理的前端处理。
原文摘要 · Abstract (English)
Classical auditory-periphery models, exemplified by Bruce et al., 2018, provide high-fidelity simulations but are stochastic and computationally demanding, limiting large-scale experimentation and low-latency use. Prior neural encoders approximate aspects of the periphery; however, few are explicitly trained to reproduce the deterministic, rate-domain neurogram , hindering like-for-like evaluation. We present a compact convolutional encoder that approximates the Bruce mean-rate pathway and maps audio to a multi-frequency neurogram. We deliberately omit stochastic spiking effects and focus on a deterministic mapping (identical outputs for identical inputs). Using a computationally efficient design, the encoder achieves close correspondence to the reference while significantly reducing computation, enabling efficient modeling and front-end processing for auditory neuroscience and audio signal processing applications.
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