Deep neural networks (DNNs) are widely used models for investigating biological visual representations. However, existing DNNs are mostly designed to analyze neural responses to static images, relying on feedforward structures and lacking physiological neuronal mechanisms. There is limited insight into how the visual cortex represents natural movie stimuli that contain context-rich information. To address these problems, this work proposes the long-range feedback spiking network (LoRaFB-SNet), which mimics top-down connections between cortical regions and incorporates spike information processing mechanisms inherent to biological neurons. Taking into account the temporal dependence of representations under movie stimuli, we present Time-Series Representational Similarity Analysis (TSRSA) to measure the similarity between model representations and visual cortical representations of mice. LoRaFB-SNet exhibits the highest level of representational similarity, outperforming other well-known and leading alternatives across various experimental paradigms, especially when representing long movie stimuli. We further conduct experiments to quantify how temporal structures (dynamic information) and static textures (static information) of the movie stimuli influence representational similarity, suggesting that our model benefits from long-range feedback to encode context-dependent representations just like the brain. Altogether, LoRaFB-SNet is highly competent in capturing both dynamic and static representations of the mouse visual cortex and contributes to the understanding of movie processing mechanisms of the visual system. Our codes are available at https://github.com/Grasshlw/SNN-Neural-Similarity-Movie.

翻译：深度神经网络（DNNs）是研究生物视觉表征的常用模型。然而，现有DNNs大多设计用于分析对静态图像的神经响应，依赖前馈结构且缺乏生理性神经元机制。对于视觉皮层如何表征包含丰富上下文信息的自然电影刺激，目前认识有限。为解决这些问题，本研究提出了长程反馈脉冲网络（LoRaFB-SNet），该网络模拟了皮层区域间的自上而下连接，并融合了生物神经元固有的脉冲信息处理机制。考虑到电影刺激下表征的时间依赖性，我们提出了时间序列表征相似性分析（TSRSA）来度量模型表征与小鼠视觉皮层表征之间的相似性。LoRaFB-SNet展现出最高的表征相似性水平，在各种实验范式下均优于其他知名且领先的替代模型，尤其是在表征长电影刺激时。我们进一步通过实验量化了电影刺激的时间结构（动态信息）与静态纹理（静态信息）如何影响表征相似性，结果表明我们的模型受益于长程反馈以编码上下文依赖的表征，这与大脑的工作机制类似。总之，LoRaFB-SNet在捕捉小鼠视觉皮层的动态与静态表征方面表现出色，并有助于理解视觉系统的电影处理机制。我们的代码可在 https://github.com/Grasshlw/SNN-Neural-Similarity-Movie 获取。