What are currently used biologically plausible computational models/frameworks of early learning in children?
Personally, I have used cascade correlation neural nets to model pronoun acquisition in children, but in hindsight, I consider this framework to be not neurobiologically plausible.
Are there existing computational frameworks that have been used for early-learning and development in children that have a reasonable biologically justification? For example: just being a neural net model is not a reasonable justification, but a neural net model witch has local update rules (like Hebbian learning) and models neurons more realistically (instead of just saying they are a sigmoid function without any further biological justification) would be fine. General non-connectionist frameworks with empirical justification are also of interest, even if they don't have a solid reductionist account.
I have asked an alternate version of this question on Linguistics.SE that focuses on language acquisition instead of general learning, but considers both children and adults.