Volume 2 - Issue 3, May - June 2026

Individual neurons in deep neural networks undergo a rich, multi-stage lifecycle—from random initialization through progressive feature specialisation, potential degradation (dying, saturated, or dormant states), and possible recovery via modern regularization or architectural interventions. Understanding these dynamics is essential for reliable training, generalization, and interpretability. This paper presents a unified framework for the neuron lifecycle, formalizes five distinct phases—Birth, Activation, Specialization, Degradation, and Recovery or Death—and analyzes the mathematical underpinnings of each. We further provide empirical analysis across activation functions (ReLU, GELU, Swish, ELU), initialization schemes (Xavier, He, LeCun), and network depths (4–256 layers), revealing how hyperparameter choices interact with neuronal fate. Practical guidelines for diagnosing and mitigating pathological lifecycle outcomes are synthesized from recent literature.

Ujjwal Kushwah, Upendra Singh Tomar, Sumit Sharma, Shivam Rawat, Shiv Kumar Sharma,"Neuron Lifecycle in Deep Networks" International Journal of Advanced Multidisciplinary Research and Educational Development, V2(3): Page(945-949) May-June 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.