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

2025-08-04

AI Summary

Current artificial neural networks lack the functional specialization and temporal coordination mechanisms inherent in biological cognition, resulting in limited generalization and adaptability. To address this, we propose the first brain-inspired ternary architecture—motivated by the brain’s tripartite functional organization (perceptual, associative, and executive)—that explicitly integrates multi-frequency neural oscillations with dynamic synaptic plasticity to model temporal coordination in cognitive processes. Methodologically, our approach unifies spiking neural networks, multi-band oscillatory dynamics, temporally adaptive synapses, and spatiotemporal coding. Experiments on visual sequential tasks demonstrate a 2.18% accuracy gain, a 48.44% reduction in computational iterations, and significantly improved alignment between model confidence and human judgments (p < 0.01), thereby enhancing both interpretability and cognitive fidelity.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Humans and AI: Brain-Sensing and Analysis
• Machine Learning: Bio-inspired Learning

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

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.

Problem

• Enhance flexible generalizable intelligence in artificial neural networks
• Integrate multi-frequency oscillations for brain-like temporal dynamics
• Improve computational efficiency and accuracy in cognitive tasks

Innovation

• Tripartite brain-inspired architecture with specialized systems
• Multi-frequency neural oscillation simulation for temporal dynamics
• Synaptic dynamic adaptation mechanisms enhancing cognition