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Abstract：
Deep convolutional neural networks (CNNs) are commonly used as computational models for the primate ventral stream, while deep spiking neural networks (SNNs) incorporated with both the temporal and spatial spiking information still lack investigation. We compared performances of SNN and CNN in prediction of visual responses to the naturalistic stimuli in area V4, inferior temporal (IT), and orbitofrontal cortex (OFC). The accuracies based on SNN were significantly higher than that of CNN in prediction of temporal-dynamic trajectory and averaged firing rate of visual response in V4 and IT. The temporal dynamics were captured by SNN for neurons with diverse temporal profiles and category selectivities, and most sensitively captured around the time of peak responses for each brain region. Consistently, SNN activities showed significantly stronger correlations with IT, V4 and OFC responses. In SNN, correlations with neural activities were stronger for later time-step features than early time-step features. The temporal-dynamic prediction was also significantly improved by considering preceding neural activities during the prediction. Thus, our study demonstrated SNN as a powerful temporal-dynamic model for cortical responses to complex naturalistic stimuli.
摘要：
深度卷积神经网络（CNN）通常用作灵长类动物腹流的计算模型，而融合了时间和空间尖峰信息的深度尖峰神经网络(SNN)仍然缺乏研究。我们比较了SNN和CNN在V4区域，下时间（IT）对自然刺激的视觉反应预测中的表现，和眶额皮质（OFC）。在V4和IT中，基于SNN的时间动态轨迹预测和视觉响应的平均激发率的准确性显着高于CNN。SNN捕获了具有不同时间轮廓和类别选择性的神经元的时间动态，并且在每个大脑区域的峰值反应时间附近最敏感地捕获。始终如一，SNN活动与IT表现出明显更强的相关性，V4和OFC响应。在SNN中,与神经活动的相关性对于较晚的时间步长特征比早期的时间步长特征更强。通过在预测过程中考虑先前的神经活动，时间动态预测也得到了显着改善。因此，我们的研究表明,SNN是皮质对复杂自然刺激反应的强大时间-动态模型.
