Biologically Inspired Neural Network Layer with Homeostatic Regulation and Adaptive Repair Mechanisms

Neural networks face persistent challenges in maintaining stability and robustness during training, particularly in noisy or high-dimensional domains like molecular analysis. Inspired by biological neural systems that leverage homeostasis and self-repair to sustain functionality, this paper proposes BioLogicalNeuron—a novel neural network layer that integrates calcium-driven homeostatic regulation, self-repair mechanisms, and dynamic stability monitoring. The layer mimics biological calcium dynamics to maintain neuronal activity within optimal ranges, proactively triggers targeted synaptic repair and adaptive noise injection to counteract degradation, and modulates learning rates via real-time health metrics. Extensive experiments across multiple molecular and chemical datasets show that BioLogicalNeuron achieves state-of-the-art break performance. The layer’s performance is particularly strong on molecular datasets, where its biological mechanisms naturally align with molecular structure learning. Through detailed analysis of calcium dynamics and health-stability relationships, this work demonstrates that BioLogicalNeuron achieves a biologically plausible balance between stability and plasticity, offering insights into both artificial and biological neural networks. These results suggest that incorporating biological mechanisms into neural architectures can lead to more robust and effective learning systems, particularly for molecular and chemical analysis tasks.