Neuron importance assessment is crucial for understanding the inner workings of artificial neural networks (ANNs) and improving their interpretability and efficiency. This paper introduces a novel approach to neuron significance assessment inspired by frequency tagging, a technique from neuroscience. By applying sinusoidal contrast modulation to image inputs and analyzing resulting neuron activations, this method enables fine-grained analysis of a network's decision-making processes. Experiments conducted with a convolutional neural network for image classification reveal notable harmonics and intermodulations in neuron-specific responses under part-based frequency tagging. These findings suggest that ANNs exhibit behavior akin to biological brains in tuning to flickering frequencies, thereby opening avenues for neuron/filter importance assessment through frequency tagging. The proposed method holds promise for applications in network pruning, and model interpretability, contributing to the advancement of explainable artificial intelligence and addressing the lack of transparency in neural networks. Future research directions include developing novel loss functions to encourage biologically plausible behavior in ANNs.

翻译：神经元重要性评估对于理解人工神经网络（ANN）的内部工作机制、提升其可解释性与效率至关重要。本文受神经科学中频率标记技术的启发，提出了一种新颖的神经元重要性评估方法。通过对图像输入施加正弦对比度调制并分析由此产生的神经元激活，该方法能够对网络决策过程进行细粒度分析。在使用卷积神经网络进行图像分类的实验中，部分基频标记下神经元特异性响应呈现出显著的谐波与互调分量。这些发现表明，人工神经网络在响应闪烁频率时表现出与生物大脑相似的行为特性，从而为通过频率标记进行神经元/滤波器重要性评估开辟了新途径。所提方法在网络剪枝、模型可解释性等应用中具有潜力，有助于推动可解释人工智能的发展，并应对神经网络缺乏透明性的问题。未来的研究方向包括设计新型损失函数以促进人工神经网络中更符合生物机制的行为模式。