The Cooperative Network Architecture: Learning Structured Networks as Representation of Sensory Patterns
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909916110782464 |
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| author | Sager, Pascal J. Deriu, Jan M. Grewe, Benjamin F. Stadelmann, Thilo von der Malsburg, Christoph |
| author_facet | Sager, Pascal J. Deriu, Jan M. Grewe, Benjamin F. Stadelmann, Thilo von der Malsburg, Christoph |
| contents | We introduce the Cooperative Network Architecture (CNA), a model that represents sensory signals using structured, recurrently connected networks of neurons, termed "nets." Nets are dynamically assembled from overlapping net fragments, which are learned based on statistical regularities in sensory input. This architecture offers robustness to noise, deformation, and generalization to out-of-distribution data, addressing challenges in current vision systems from a novel perspective. We demonstrate that net fragments can be learned without supervision and flexibly recombined to encode novel patterns, enabling figure completion and resilience to noise. Our findings establish CNA as a promising paradigm for developing neural representations that integrate local feature processing with global structure formation, providing a foundation for future research on invariant object recognition. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_05650 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Cooperative Network Architecture: Learning Structured Networks as Representation of Sensory Patterns Sager, Pascal J. Deriu, Jan M. Grewe, Benjamin F. Stadelmann, Thilo von der Malsburg, Christoph Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Neural and Evolutionary Computing We introduce the Cooperative Network Architecture (CNA), a model that represents sensory signals using structured, recurrently connected networks of neurons, termed "nets." Nets are dynamically assembled from overlapping net fragments, which are learned based on statistical regularities in sensory input. This architecture offers robustness to noise, deformation, and generalization to out-of-distribution data, addressing challenges in current vision systems from a novel perspective. We demonstrate that net fragments can be learned without supervision and flexibly recombined to encode novel patterns, enabling figure completion and resilience to noise. Our findings establish CNA as a promising paradigm for developing neural representations that integrate local feature processing with global structure formation, providing a foundation for future research on invariant object recognition. |
| title | The Cooperative Network Architecture: Learning Structured Networks as Representation of Sensory Patterns |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2407.05650 |