A unified theoretical approach for biological cognition and learning

Large-scale neural models are needed in order to understand the biological underpinnings of complex cognitive behavior. Good methods for constructing such models should provide for: first, abstraction (analysis across levels of description); second, integration (incorporation of simpler models to build more complex ones); third, empirical contact (using and comparing to a wide variety of neural data); and fourth, account for the varieties of learning. In this review we evaluate three prominent recent methods for constructing neural models using these four criteria. Each of these methods is being actively developed and demonstrates clear strengths along some of these criteria.