Neuro-Hormonal Networks: A Bio-Inspired Architecture forEnhanced Deep Learning Performance

In this work, neuro-hormonal networks (NHNs) are presented which are a novel bioinspireddeep learning architecture that integrates dynamic neuro-modulation mechanismsto achieve superior performance in deep learning tasks. Our approach addresses fundamentallimitations in conventional neural networks and how they are enhanced by incorporatingdopamine and cortisol-like hormonal pathways that adaptively modulate synaptic weightsduring training, mirroring biological neuro-endocrine systems. The mathematical frameworkis grounded in delay differential equations, control theory, and weighted logic networks,implementing three distinct modulation types: accelerators for enhanced plasticity,suppressors for homeostatic regulation, and stabilizers for output regularization. The multihormonalmodulation component dynamically adjusts effective weights. Thus, enablingadaptive learning that responds to both current performance and historical training dynamics.Comprehensive evaluation across four benchmark datasets demonstrates consistentand statistically significant improvements: CIFAR-10 (+5.36pp), CIFAR-100 (+6.57pp),Fashion-MNIST (+0.67pp), and SVHN (+0.09pp). McNemar’s test validation confirmsstatistical significance (p ¡ 0.001) for three of four datasets, with particularly strong benefitsobserved for complex, multi-class classification tasks. Advanced performance metricsinclude ROC AUC (≥ 0.99) and PR AUC (≥ 0.95) demonstrating superior discriminationcapability and robustness across evaluation scenarios. The architecture exhibits enhancedconvergence stability, improved generalization capability, and interpretable adaptation patternsthat correlate with training dynamics. While incurring an average of 2.96× computationaloverhead per epoch, the biological plausibility and consistent performance gainsvalidate the practical effectiveness of neuro-endocrine principles in artificial intelligence.Our work establishes a rigorous mathematical foundation for hormonal computing anddemonstrates how multi-timescale biological dynamics can be successfully translated intoimproved deep learning architectures. To the best of our knowledge, the NHN is thefirst novel work to establish and implement the neuro-hormonal modulation indeep learning architectures with rigorous mathematical background, advancedimplementations and novel results.