Theoretical understanding of neurodevelopmental conditions (NCs) has shifted from a categorical approach to a dimensional one, characterized by an acceptance of comorbidity and heterogeneity. Previous computational modelling of NCs has tended only to accommodate categorical views. The current work presents a mechanistic simulation framework that fits with the dimensional view, using artificial neural networks to model populations of learners, with underlying causes of variation in developmental outcomes viewed as continuous, polygenic, and in part environmental. We show how the dimensional and categorical approaches can be linked using latent profile analysis and outlier methods, recovering profiles and specific deficits from dimensional variation. We show how altering the distribution of hyper-parameters shifts the population composition of developmental profiles and frequencies of deficit patterns, and we test their robustness to stochastic factors.