Spiking neural networks (SNNs) are receiving increased attention as a means to develop "biologically plausible" machine learning models. These networks mimic synaptic connections in the human brain and produce spike trains, which can be approximated by binary values, precluding high computational cost with floating-point arithmetic circuits. Recently, the addition of convolutional layers to combine the feature extraction power of convolutional networks with the computational efficiency of SNNs has been introduced. In this paper, the feasibility of using a convolutional spiking neural network (CSNN) as a classifier to detect anticipatory slow cortical potentials related to braking intention in human participants using an electroencephalogram (EEG) was studied. The EEG data was collected during an experiment wherein participants operated a remote controlled vehicle on a testbed designed to simulate an urban environment. Participants were alerted to an incoming braking event via an audio countdown to elicit anticipatory potentials that were then measured using an EEG. The CSNN's performance was compared to a standard convolutional neural network (CNN) and three graph neural networks (GNNs) via 10-fold cross-validation. The results showed that the CSNN outperformed the other neural networks.

翻译：脉冲神经网络（SNNs）作为一种构建“生物可解释”机器学习模型的方法日益受到关注。这类网络模拟人脑中的突触连接并产生可被二进制值近似的脉冲序列，从而避免了浮点运算电路带来的高计算成本。近期，研究人员引入卷积层以结合卷积网络的特征提取能力与SNN的计算高效性。本文研究了使用卷积脉冲神经网络（CSNN）作为分类器，通过脑电图（EEG）检测人类被试者与刹车意图相关的预期性慢皮层电位的可行性。实验过程中，被试者在模拟城市环境的测试平台上操作遥控车辆，通过音频倒计时提示即将发生的刹车事件以诱发出预期性电位，并由EEG记录。通过10折交叉验证，将CSNN的性能与标准卷积神经网络（CNN）及三种图神经网络（GNN）进行了对比。结果表明，CSNN的性能优于其他神经网络。