Neural Manifolds and Cognitive Consistency: A New Approach to Memory Consolidation in Artificial Systems
This work addresses memory consolidation for scalable neuromorphic architectures, bridging neuroscience and AI to enable more robust learning, but it appears incremental as it builds on existing biological and theoretical concepts.
The authors tackled the problem of memory consolidation in artificial systems by introducing a mathematical framework that unifies neural dynamics and cognitive constraints, resulting in a model that reproduces biological features like sharp wave-ripples and enhances network interpretability.
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.