# Neural Manifolds and Cognitive Consistency: A New Approach to Memory Consolidation in Artificial Systems

arXiv: https://arxiv.org/abs/2503.01867 · HTML: https://mlsanity.com/paper/2503.01867/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 25 Feb 2025.

## Builds on (most cited first, 3 of 3)

- [Quantum Embedding Search for Quantum Machine Learning](https://mlsanity.com/paper/2105.11853/) (2021)
- [Contrastive Self-supervised Neural Architecture Search](https://mlsanity.com/paper/2102.10557/) (2021)
- [Biomarker Discovery with Quantum Neural Networks: A Case-study in CTLA4-Activation Pathways](https://mlsanity.com/paper/2306.01745/) (2023)

## Abstract (arXiv)

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.