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| Format: | Preprint |
| Published: |
2011
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/1103.1587 |
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| _version_ | 1866912715309580288 |
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| author | Li, Xin |
| author_facet | Li, Xin |
| contents | We propose a formal foundation for cognition rooted in algebraic topology, built on a Homological Parity Principle. This posits that even-dimensional homology represents stable Structure/Context (e.g., generative models), while odd-dimensional homology represents dynamic Flow/Content (e.g., sensory/memory data). Cognition is governed by the Context-Content Uncertainty Principle (CCUP), a dynamical cycle aligning these parities. This framework distinguishes two modes: Inference (waking), where the scaffold predicts the flow (a Context-before-Content process); and Learning (sleep), an inverted Structure-before-Specificity process where memory traces sculpt the scaffold. This parity interpretation unifies cognitive functions like semantic and episodic memory and provides a structural generalization of existing theories, recasting Friston's Free Energy Principle and Tonini's Integrated Information in topological terms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1103_1587 |
| institution | arXiv |
| publishDate | 2011 |
| record_format | arxiv |
| spellingShingle | On the Topological Foundation of Learning and Memory Li, Xin Computer Vision and Pattern Recognition We propose a formal foundation for cognition rooted in algebraic topology, built on a Homological Parity Principle. This posits that even-dimensional homology represents stable Structure/Context (e.g., generative models), while odd-dimensional homology represents dynamic Flow/Content (e.g., sensory/memory data). Cognition is governed by the Context-Content Uncertainty Principle (CCUP), a dynamical cycle aligning these parities. This framework distinguishes two modes: Inference (waking), where the scaffold predicts the flow (a Context-before-Content process); and Learning (sleep), an inverted Structure-before-Specificity process where memory traces sculpt the scaffold. This parity interpretation unifies cognitive functions like semantic and episodic memory and provides a structural generalization of existing theories, recasting Friston's Free Energy Principle and Tonini's Integrated Information in topological terms. |
| title | On the Topological Foundation of Learning and Memory |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/1103.1587 |