Neuromorphic Computing with Multi-Frequency Oscillations: A Bio-Inspired Approach to Artificial Intelligence (2508.02191v1)

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.

• The paper introduces a tripartite brain-inspired architecture that improves image classification accuracy by 2.18% using multi-frequency oscillations.
• It employs synaptic dynamic adaptation and frequency band synchronization to enhance computational efficiency, reducing iterations by up to 48.44%.
• Ablation studies confirm that each specialized component contributes to a more human-like and adaptive AI system.

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

This paper introduces a novel Tripartite Brain-Inspired Architecture, integrating insights from biological cognition into artificial intelligence systems. By simulating multi-frequency neural oscillations and synaptic dynamic adaptation mechanisms, the architecture aims to enhance artificial cognition's flexibility and efficiency, closely aligning AI systems with the adaptive and generalizable intelligence of biological systems.

Biological Inspiration in Neural Architecture Design

Tripartite Architecture Overview

The proposed architecture is organized into three functionally specialized components: Perceptual Feature Processing System (PFPS), Auxiliary Modulation System (AMS), and Executive Decision System (EDS). Each system is modeled on distinct neurobiological regions responsible for sensory processing, modulatory control, and decision-making, respectively. This approach reflects brain regions' functional specialization and promotes a coordinated operational framework that mimics biological cognition.

Figure 1: Tripartite Brain Cognitive Architecture in the Human Brain. In visual tasks, three functionally specialized systems — Perceptual, Auxiliary, and Executive — reflect the regional collaboration of biological neural organization.

Temporal Dynamics Integration

Neural Oscillation and Synaptic Adaptation

Temporal dynamics, crucial for biological cognition, are elegantly incorporated through multi-frequency neural oscillations and synaptic dynamic adaptation.

• Neural Oscillation: By assigning neurons to discrete frequency bands ($\gamma$, $\beta$, $\alpha$, $\theta$), the architecture achieves temporal synchronization akin to the brain's rhythmic activity. This synchronization binds information processing across various timescales, enhancing efficiency.
• Synaptic Dynamic Adaptation: Inspired by synaptic plasticity, the architecture modulates synaptic efficiency based on input complexity. This approach mimics biological systems' adaptability, offering a computational means to fine-tune processing accuracy versus resource allocation dynamically.

  Figure 2: Tripartite Brain-Inspired Architecture with neural oscillation and adaptive processing. This illustrates hierarchical processing, oscillation mechanisms, and adaptive synaptic pathways.

Experimental Validation and Performance Metrics

The architecture was rigorously validated on visual processing tasks, demonstrating superior performance metrics in image classification accuracy and computational efficiency.

• Accuracy Improvement: A marked increase in classification accuracy was observed, with improvements of up to 2.18% compared to state-of-the-art methods, including CTM.
• Computational Efficiency: The architecture reduced the required computation iterations by up to 48.44%, underscoring the efficiency gains from temporal synchronization and adaptive processing.

  Figure 3: Robustness to input noise and human-alignment analysis. The architecture shows consistent performance under noise and aligns closely with human categorization patterns.

Ablation Studies and Analysis

To further elucidate the contributions of individual components:

• Neural Oscillation and Modulation: Ablation studies showed improved both accuracy and efficiency, particularly with full neuromodulatory integration.
• Synaptic and Neuron Density: Enhancements in synaptic complexity and increased neuron density lead to better performance and reduced iterations, reflecting the importance of robust interconnections and resource distribution akin to biological systems.

  Figure 4: Ablation studies and visualization of Tripartite Brain-Inspired Architecture. This includes performance impacts due to neural oscillation, synaptic adaptation, neuron density variations, and associated visualizations of attention maps and phase coherence patterns.

Implications and Future Directions

The architecture's integration of neuromorphic principles represents a significant step toward bridging the gap between artificial and biological cognition. Future research may extend these principles to broader cognitive domains, such as language processing and complex decision-making, potentially leading to AI systems with more human-like adaptability and generalization capabilities. Additionally, the architecture's demonstrated alignment with human categorization under uncertainty suggests promising applications in fields requiring human-like interpretation and decision processes.

Conclusion

This Tripartite Brain-Inspired Architecture showcases the potential of bio-inspired design principles in advancing AI towards more intelligent, adaptable, and efficient systems by leveraging functional specialization and temporal dynamics. The alignment with human cognitive patterns further enriches the architecture's applicability in diverse real-world scenarios. The research lays a foundational framework for future exploration in enhancing AI's biological congruence.