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Deep neural networks (DNNs) are widely used models for investigatingbiological visual representations. However, existing DNNs are mostly designedto analyze neural responses to static images, relying on feedforward structuresand lacking physiological neuronal mechanisms. There is limited insight intohow the visual cortex represents natural movie stimuli that containcontext-rich information. To address these problems, this work proposes thelong-range feedback spiking network (LoRaFB-SNet), which mimics top-downconnections between cortical regions and incorporates spike informationprocessing mechanisms inherent to biological neurons. Taking into account thetemporal dependence of representations under movie stimuli, we presentTime-Series Representational Similarity Analysis (TSRSA) to measure thesimilarity between model representations and visual cortical representations ofmice. LoRaFB-SNet exhibits the highest level of representational similarity,outperforming other well-known and leading alternatives across variousexperimental paradigms, especially when representing long movie stimuli. Wefurther conduct experiments to quantify how temporal structures (dynamicinformation) and static textures (static information) of the movie stimuliinfluence representational similarity, suggesting that our model benefits fromlong-range feedback to encode context-dependent representations just like thebrain. Altogether, LoRaFB-SNet is highly competent in capturing both dynamicand static representations of the mouse visual cortex and contributes to theunderstanding of movie processing mechanisms of the visual system. Our codesare available at https://github.com/Grasshlw/SNN-Neural-Similarity-Movie.

Long-Range Feedback Spiking Network Captures Dynamic and Static Representations of the Visual Cortex under Movie Stimuli