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）在模拟灵长类和啮齿类动物的视觉通路中扮演着重要角色。然而，与生物神经元相比，这些模型极大地简化了神经元的计算特性。相比之下，脉冲神经网络（SNNs）是更具生物合理性的模型，因为脉冲神经元像生物神经元一样，通过脉冲的时间序列来编码信息。然而，目前尚缺乏利用深度SNNs模型研究视觉通路的工作。在本研究中，我们首次利用深度SNNs模拟视觉皮层，并同时采用一系列最先进的深度CNNs和ViTs进行对比。使用三种相似性指标，我们对从两个物种、三种刺激类型下收集的三个神经数据集进行了神经表征相似性实验。基于广泛的相似性分析，我们进一步研究了跨物种的功能层次和机制。几乎所有SNNs的相似性得分均高于其对应的CNNs，平均高出6.6%。具有最高相似性得分的层深度在小鼠皮层区域间差异很小，但在猕猴区域间差异显著，表明小鼠的视觉处理结构在区域上比猕猴更具同质性。此外，在部分最类脑的小鼠神经网络中观察到的多分支结构，为小鼠大脑中存在的并行处理流提供了计算证据；而模型在不同刺激下拟合猕猴神经表征的差异表现，则展示了猕猴信息处理的功能特异性。综上所述，我们的研究表明，SNNs是更好模拟和解释视觉系统功能层次与机制的极具潜力的候选模型。