English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In the real world, visual stimuli received by the biological visual systemare predominantly dynamic rather than static. A better understanding of how thevisual cortex represents movie stimuli could provide deeper insight into theinformation processing mechanisms of the visual system. Although some progresshas been made in modeling neural responses to natural movies with deep neuralnetworks, the visual representations of static and dynamic information undersuch time-series visual stimuli remain to be further explored. In this work,considering abundant recurrent connections in the mouse visual system, wedesign a recurrent module based on the hierarchy of the mouse cortex and add itinto Deep Spiking Neural Networks, which have been demonstrated to be a morecompelling computational model for the visual cortex. Using Time-SeriesRepresentational Similarity Analysis, we measure the representationalsimilarity between networks and mouse cortical regions under natural moviestimuli. Subsequently, we conduct a comparison of the representationalsimilarity across recurrent/feedforward networks and image/video trainingtasks. Trained on the video action recognition task, recurrent SNN achieves thehighest representational similarity and significantly outperforms feedforwardSNN trained on the same task by 15% and the recurrent SNN trained on the imageclassification task by 8%. We investigate how static and dynamicrepresentations of SNNs influence the similarity, as a way to explain theimportance of these two forms of representations in biological neural coding.Taken together, our work is the first to apply deep recurrent SNNs to model themouse visual cortex under movie stimuli and we establish that these networksare competent to capture both static and dynamic representations and makecontributions to understanding the movie information processing mechanisms ofthe visual cortex.

Deep recurrent spiking neural networks capture both static and dynamic representations of the visual cortex under movie stimuli