Deep artificial neural networks (ANNs) play a major role in modeling the visual pathways of primate and rodent. However, they highly simplify the computational properties of neurons compared to their biological counterparts. Instead, Spiking Neural Networks (SNNs) are more biologically plausible models since spiking neurons encode information with time sequences of spikes, just like biological neurons do. However, there is a lack of studies on visual pathways with deep SNNs models. In this study, we model the visual cortex with deep SNNs for the first time, and also with a wide range of state-of-the-art deep CNNs and ViTs for comparison. Using three similarity metrics, we conduct neural representation similarity experiments on three neural datasets collected from two species under three types of stimuli. Based on extensive similarity analyses, we further investigate the functional hierarchy and mechanisms across species. Almost all similarity scores of SNNs are higher than their counterparts of CNNs with an average of 6.6%. Depths of the layers with the highest similarity scores exhibit little differences across mouse cortical regions, but vary significantly across macaque regions, suggesting that the visual processing structure of mice is more regionally homogeneous than that of macaques. Besides, the multi-branch structures observed in some top mouse brain-like neural networks provide computational evidence of parallel processing streams in mice, and the different performance in fitting macaque neural representations under different stimuli exhibits the functional specialization of information processing in macaques. Taken together, our study demonstrates that SNNs could serve as promising candidates to better model and explain the functional hierarchy and mechanisms of the visual system.

翻译：深度人工神经网络（ANNs）在模拟灵长类与啮齿类动物视觉通路中发挥着重要作用。然而，相较于生物神经元，这些网络极大简化了神经元的计算特性。相比之下，脉冲神经网络（SNNs）由于脉冲神经元像生物神经元一样通过脉冲时间序列编码信息，因而成为更具生物合理性的模型。但当前尚缺乏基于深度SNNs模型研究视觉通路的工作。本研究首次采用深度SNNs模拟视觉皮层，同时使用多种最新深度卷积神经网络（CNNs）与视觉变换器（ViTs）进行对比。通过三种相似度指标，我们对采集自两种物种、三类刺激条件下的三个神经数据集进行了神经表征相似度实验。基于广泛的相似性分析，我们进一步探究了跨物种的功能层级与机制。几乎所有SNNs的相似度评分均高于对应的CNNs，平均高出6.6%。小鼠皮层区域中最高相似度得分对应的层深差异极小，而猕猴各区域间则差异显著，表明小鼠的视觉处理结构在区域层面比猕猴更具同质性。此外，部分最优拟合小鼠脑功能的神经网络中存在的多分支结构，为小鼠脑内存在平行处理流提供了计算证据；而针对不同刺激条件下拟合猕猴神经表征的性能差异，则体现了猕猴信息处理的功能特异性。综上，本研究证明SNNs可作为更优候选模型来解释视觉系统的功能层级与机制。