Neural Manifolds and Cognitive Consistency: A New Approach to Memory Consolidation in Artificial Systems
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arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909524770684928 |
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| author | Nguyen, Phuong-Nam |
| author_facet | Nguyen, Phuong-Nam |
| contents | We introduce a novel mathematical framework that unifies neural population dynamics, hippocampal sharp wave-ripple (SpWR) generation, and cognitive consistency constraints inspired by Heider's theory. Our model leverages low-dimensional manifold representations to capture structured neural drift and incorporates a balance energy function to enforce coherent synaptic interactions, effectively simulating the memory consolidation processes observed in biological systems. Simulation results demonstrate that our approach not only reproduces key features of SpWR events but also enhances network interpretability. This work paves the way for scalable neuromorphic architectures that bridge neuroscience and artificial intelligence, offering more robust and adaptive learning mechanisms for future intelligent systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_01867 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neural Manifolds and Cognitive Consistency: A New Approach to Memory Consolidation in Artificial Systems Nguyen, Phuong-Nam Artificial Intelligence Neural and Evolutionary Computing Neurons and Cognition We introduce a novel mathematical framework that unifies neural population dynamics, hippocampal sharp wave-ripple (SpWR) generation, and cognitive consistency constraints inspired by Heider's theory. Our model leverages low-dimensional manifold representations to capture structured neural drift and incorporates a balance energy function to enforce coherent synaptic interactions, effectively simulating the memory consolidation processes observed in biological systems. Simulation results demonstrate that our approach not only reproduces key features of SpWR events but also enhances network interpretability. This work paves the way for scalable neuromorphic architectures that bridge neuroscience and artificial intelligence, offering more robust and adaptive learning mechanisms for future intelligent systems. |
| title | Neural Manifolds and Cognitive Consistency: A New Approach to Memory Consolidation in Artificial Systems |
| topic | Artificial Intelligence Neural and Evolutionary Computing Neurons and Cognition |
| url | https://arxiv.org/abs/2503.01867 |