The data generated from sensors placed in and around complex engineering systems is increasing exponentially, and techniques are being developed to model the behavior of the system and learn about their failure and degradation over lifetime. Training these data driven models places requirements constraints on the data with regards to its availability, distribution and training process. The aim of the paper is to highlight the limitations of the model training methodologies and justify the need for a neuro-plausible framework for complex engineering systems. Viewing complex engineering systems as biological organisms facilitate in applying the learning from biological understandings to engineering problems. The paper explores engineering challenges from a biological standpoint, particularly through the lens of neuroscience, offering a biological perspective rather than a traditional engineering approach to solving complex engineering system problems.