Neuromorphic Computing with Multi-Frequency Oscillations: A Bio-Inspired Approach to Artificial Intelligence
This work addresses the problem of achieving more flexible and efficient artificial intelligence, particularly for visual processing tasks, though it is incremental in building on brain-inspired concepts.
The paper tackles the limited flexible intelligence of artificial neural networks by proposing a brain-inspired architecture with functional specialization and multi-frequency oscillations, resulting in 2.18% accuracy improvements and 48.44% reduction in computation iterations compared to state-of-the-art methods.
Despite remarkable capabilities, artificial neural networks exhibit limited flexible, generalizable intelligence. This limitation stems from their fundamental divergence from biological cognition that overlooks both neural regions' functional specialization and the temporal dynamics critical for coordinating these specialized systems. We propose a tripartite brain-inspired architecture comprising functionally specialized perceptual, auxiliary, and executive systems. Moreover, the integration of temporal dynamics through the simulation of multi-frequency neural oscillation and synaptic dynamic adaptation mechanisms enhances the architecture, thereby enabling more flexible and efficient artificial cognition. Initial evaluations demonstrate superior performance compared to state-of-the-art temporal processing approaches, with 2.18\% accuracy improvements while reducing required computation iterations by 48.44\%, and achieving higher correlation with human confidence patterns. Though currently demonstrated on visual processing tasks, this architecture establishes a theoretical foundation for brain-like intelligence across cognitive domains, potentially bridging the gap between artificial and biological intelligence.