Exposure to prosocial models is commonly used to foster prosocial behavior in various domains of society. Although their translational value has been challenged in several cases, rodents, in particular Long Evans rats, are invaluable when modeling the context of human behavior in alternate rearing-environments. Rodent models are chosen for convenience, i.e., availability rather than neurotechnologically enabled neurosurgical interventions involving the human brain. Apart from a general discussion on translational success or failure, the complex bidirectional process involving multidisciplinary research integration often requires new data science methods. This research innovates by intersecting the disciplines of neuroscience and artificial intelligence (AI). But the novel union of AI, data science, and neuro-behavioral experiments comes with benefits and the potential for harm. We conclude by reporting the appropriateness of the enhanced K4-RANN as an effective ML algorithm for estimating the nonlinear regression weights of neural activity in trait-bred Long Evans rats subjected to alternate rearing environments. However, the study finds this conclusion is most true under the principle of universal approximation for algorithmic parameter settings.