AI 中文总结
研究基于功能磁共振成像的视觉语义解码，对比人工神经网络基线与四个脉冲神经网络变体，发现SNN衍生特征与fMRI响应更强对齐，提升了解码性能，表明其是有效大脑可解码视觉表示，凸显目标特征设计对该解码的重要性。
AI 中文摘要
基于功能磁共振成像（fMRI）的视觉解码旨在从测量的大脑活动中恢复视觉信息，通常是将fMRI响应映射到潜在视觉特征以进行下游解码任务。现有方法大多学习从fMRI响应到人工神经网络（ANN）提取的视觉特征的映射，但尚不清楚ANN衍生的特征是否为大脑解码提供合适目标。本研究调查脉冲神经网络（SNN）衍生的视觉特征作为基于fMRI的视觉解码的替代目标。将ANN基线与来自同一架构家族的四个SNN变体比较，它们的脉冲动力学不同。所有模型使用相同的L2正则化线性fMRI到特征解码器，仅改变用作回归目标的特征向量。与ANN基线相比，SNN衍生的特征与fMRI响应有更强对齐并提高视觉语义解码性能。例如，在GoD数据集上，SNN衍生的特征将特征预测误差从0.7707降至0.0282，将top-1语义解码准确率从0.1800提高到0.4400。消融结果表明脉冲神经动力学和时间模拟步骤都促成了观察到的优势。这些发现支持SNN衍生的特征作为有效的大脑可解码视觉表示，并突出目标特征设计是基于fMRI的视觉解码的重要组成部分。
英文摘要
Functional magnetic resonance imaging (fMRI)-based visual decoding aims to recover visual information from measured brain activity, commonly by mapping fMRI responses into latent visual features for downstream decoding tasks. Most existing methods learn mappings from fMRI responses to visual features extracted by artificial neural networks (ANNs), yet it remains unclear whether ANN-derived features provide suitable targets for brain decoding. In this study, we investigate spiking neural network (SNN)-derived visual features as alternative targets for fMRI-based visual decoding. We compare an ANN baseline with four SNN variants from the same architectural family, which differ in their spiking dynamics. To isolate the effect of the target features, all models use the same L2-regularized linear fMRI-to-feature decoder, while only the feature vectors used as regression targets are varied. Compared with the ANN baseline, SNN-derived features exhibit stronger alignment with fMRI responses and improve visual semantic decoding performance. For instance, on the GoD dataset, SNN-derived features reduce feature-prediction error from 0.7707 to 0.0282 and improve top-1 semantic decoding accuracy from 0.1800 to 0.4400. Ablation results further indicate that both spiking neural dynamics and temporal simulation steps contribute to the observed advantage. These findings support SNN-derived features as effective brain-decodable visual representations and highlight target feature design as an important component of fMRI-based visual decoding.
Comments10 pages, 6 figures
