Multi-Scale Temporal Homeostasis Enables Efficient and Robust Neural Networks
Published in arXiv preprint, 2025
This preprint introduces multi-scale temporal homeostasis as a mechanism for enhancing the efficiency and robustness of artificial neural networks. Inspired by biological nervous systems that coordinate activity across multiple temporal scales, the proposed framework enables more stable training dynamics and improved resilience under adverse conditions.
Author:
- MD Azizul Hakim (Lead Author, Bangladesh Sweden Polytechnic Institute, Bangladesh)
arXiv: 2602.07009
Key Contributions:
- Introduced multi-scale temporal homeostasis as a bio-inspired mechanism for neural network stability
- Demonstrated improved efficiency and robustness across benchmark evaluations
- Extended the theoretical foundation for biologically-faithful AI architectures
- Sole-author publication demonstrating independent research capability
Recommended citation: Hakim, MD Azizul (2025). "Multi-Scale Temporal Homeostasis Enables Efficient and Robust Neural Networks." arXiv preprint. arXiv:2602.07009
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