DNA-Inspired Computational Substrates for Resilient Hybrid Neural Architectures

Contemporary neural architectures exhibit persistent vulnerabilities to catastrophic forgetting, distributional drift, adversarial perturbations, and parameter degradation over extended training or deployment. In contrast, biological DNA processing achieves near-perfect fidelity through multi-layered mechanisms operating at the substrate level: immediate proofreading during replication, post-replication mismatch and excision repair, degenerate coding for redundancy, and dynamic epigenetic regulation. This paper proposes a suite of DNA-inspired computational substrates as foundational layers for hybrid neural systems, translating these biological processes into practical parameter-management protocols. Key contributions include: (1) real-time proofreading analogs for weight updates via probabilistic validation and rollback; (2) ensemble consensus mechanisms mimicking mismatch repair to detect and excise inconsistencies; (3) degenerate representation schemes enabling redundant pathways against single-point failures; and (4) adaptive “sealing” of stabilized parameters to prevent overwriting, facilitating long-term integrity without continual retraining. Quantitative evaluations on benchmark tasks (e.g., continual learning splits, adversarial robustness suites) demonstrate 40–60% reductions in forgetting rates and enhanced stability under noise, with pseudocode for seamless integration into transformer and convolutional models. By embedding biological fidelity directly at the computational substrate, this framework provides a scalable foundation for self-healing, efficient, and secure artificial general intelligence systems.