Neuromorphic Computing with Multi-Frequency Oscillations: A Bio-Inspired Approach to Artificial Intelligence

TL;DR

This paper introduces a bio-inspired neuromorphic architecture utilizing multi-frequency neural oscillations and specialized brain regions to enhance artificial intelligence's flexibility and efficiency, outperforming existing temporal processing methods.

Contribution

It presents a novel tripartite brain-inspired architecture with temporal dynamics, improving artificial cognition and establishing a theoretical foundation for brain-like intelligence.

Findings

2.18% accuracy improvement over state-of-the-art methods

48.44% reduction in computation iterations

Higher correlation with human confidence patterns

Abstract

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.