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：深度人工神经网络在模拟灵长类和啮齿类动物的视觉通路中发挥着重要作用。然而，与生物学神经元相比，它们极大简化了神经元的计算特性。相比之下，脉冲神经网络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，因此是更具生物学合理性的模型。然而，目前尚缺乏基于深度SNN模型构建视觉通路的研究。本研究首次采用深度SNN模拟视觉皮层，并同时使用多种最先进的深度CNN和ViT进行对比。通过三种相似度度量方法，我们在两种物种在三种刺激条件下收集的三种神经数据集上开展了神经表征相似性实验。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SNN的相似性得分均高于对应的CNN，平均高出6.6%。在最高相似性得分层对应的深度方面，小鼠皮层区域间差异极小，而猕猴各区域间差异显著，这表明小鼠的视觉处理结构在区域层面上比猕猴更加同质化。此外，部分最接近小鼠脑神经网络的模型所展现的多分支结构，为小鼠并行处理流的存在提供了计算证据；而不同刺激下拟合猕猴神经表征的差异性能，则体现了猕猴信息处理的功能特化。综上，本研究表明SNN可作为更优模型来模拟和解释视觉系统的功能层级与机制。