In the real world, visual stimuli received by the biological visual system are predominantly dynamic rather than static. A better understanding of how the visual cortex represents movie stimuli could provide deeper insight into the information processing mechanisms of the visual system. Although some progress has been made in modeling neural responses to natural movies with deep neural networks, the visual representations of static and dynamic information under such time-series visual stimuli remain to be further explored. In this work, considering abundant recurrent connections in the mouse visual system, we design a recurrent module based on the hierarchy of the mouse cortex and add it into Deep Spiking Neural Networks, which have been demonstrated to be a more compelling computational model for the visual cortex. Using Time-Series Representational Similarity Analysis, we measure the representational similarity between networks and mouse cortical regions under natural movie stimuli. Subsequently, we conduct a comparison of the representational similarity across recurrent/feedforward networks and image/video training tasks. Trained on the video action recognition task, recurrent SNN achieves the highest representational similarity and significantly outperforms feedforward SNN trained on the same task by 15% and the recurrent SNN trained on the image classification task by 8%. We investigate how static and dynamic representations of SNNs influence the similarity, as a way to explain the importance of these two forms of representations in biological neural coding. Taken together, our work is the first to apply deep recurrent SNNs to model the mouse visual cortex under movie stimuli and we establish that these networks are competent to capture both static and dynamic representations and make contributions to understanding the movie information processing mechanisms of the visual cortex.

翻译：在真实世界中，生物视觉系统接收的视觉刺激以动态信息为主而非静态。深入理解视觉皮层如何表征电影刺激，有助于揭示视觉系统的信息处理机制。尽管利用深度神经网络对自然电影引发的神经响应建模已取得一定进展，但此类时间序列视觉刺激下静态与动态信息的视觉表征仍有待进一步探索。本研究基于小鼠视觉系统中丰富的递归连接特性，依据小鼠皮层层级结构设计递归模块，并将其融入已被证实为更具说服力的视觉皮层计算模型——深度脉冲神经网络。通过时间序列表征相似性分析，我们测量了自然电影刺激下网络与小鼠皮层区域之间的表征相似度。随后，我们对递归/前馈网络以及图像/视频训练任务下的表征相似性进行了比较。在视频动作识别任务上训练的递归脉冲神经网络取得了最高的表征相似度，其性能显著优于同任务训练的前馈脉冲神经网络（提升15%）以及图像分类任务训练的递归脉冲神经网络（提升8%）。我们探究了脉冲神经网络的静态与动态表征如何影响相似度，以此解释这两种表征形式在生物神经编码中的重要性。综上所述，本研究首次将深度递归脉冲神经网络应用于建模电影刺激下的小鼠视觉皮层，证实该类网络能够有效捕获静态与动态表征，为理解视觉皮层的电影信息处理机制作出贡献。