Imagine a bustling city with its intricate network of streets and buildings. Just as different areas in the city have specific functions (like residential neighborhoods or commercial districts), the brain’s visual system has hierarchical structures that are responsible for different tasks. But how are these functions assigned? That’s the question scientists have been grappling with. The concept of spatio-temporally efficient coding may hold the key. It means using resources effectively, both in terms of neural activity space and processing time, to optimize information transmission across these hierarchical structures. By minimizing temporal differences in neural responses and maximizing entropy in neural representations, this coding principle ensures accurate assignment of functions to the visual system. Simulations have shown that this coding method successfully assigns appropriate neural representations to visual scenes and predicts phenomena like deviations in neural responses and bias in preferred orientations. By delving deeper into the computational processes of hierarchical brain structures, we can gain a better understanding of how our visual system works. So, get ready to embark on a journey through fascinating research on spatio-temporally efficient coding!

Hierarchical structures constitute a wide array of brain areas, including the visual system. One of the important questions regarding visual hierarchical structures is to identify computational principles for assigning functions that represent the external world to hierarchical structures of the visual system. Given that visual hierarchical structures contain both bottom-up and top-down pathways, the derived principles should encompass these bidirectional pathways. However, existing principles such as predictive coding do not provide an effective principle for bidirectional pathways. Therefore, we propose a novel computational principle for visual hierarchical structures as spatio-temporally efficient coding underscored by the efficient use of given resources in both neural activity space and processing time. This coding principle optimises bidirectional information transmissions over hierarchical structures by simultaneously minimising temporal differences in neural responses and maximising entropy in neural representations. Simulations demonstrated that the proposed spatio-temporally efficient coding was able to assign the function of appropriate neural representations of natural visual scenes to visual hierarchical structures. Furthermore, spatio-temporally efficient coding was able to predict well-known phenomena, including deviations in neural responses to unlearned inputs and bias in preferred orientations. Our proposed spatio-temporally efficient coding may facilitate deeper mechanistic understanding of the computational processes of hierarchical brain structures.

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