ArticleFrontiers in computational neuroscience2022
Spatio-Temporally Efficient Coding Assigns Functions to Hierarchical Structures of the Visual System.
Article in Frontiers in computational neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Adaptation of the inferior temporal neurons and efficient visual processing.Frontiers in behavioral neuroscience · 2024Review
- Robust and efficient representations of dynamic stimuli in hierarchical neural networks via temporal smoothing.Frontiers in computational neuroscience · 2023Article
- Spatio-Temporally Efficient Coding Assigns Functions to Hierarchical Structures of the Visual System.Frontiers in computational neuroscience · 2022Article
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2 authors.
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Abstract
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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