Computational neuroscience is a branch of neuroscience which employs mathematical models, theoretical analysis and abstractions of the brain to understand the principles that govern the development, structure, physiology and cognitive abilities of the nervous system. It employs computational simulations to validate and solve the mathematical models that - are in most cases - too complex to be solved analytically.
Computational neuroscience focuses on the description of biologically plausible neurons (and neural systems) and their physiology and dynamics, and it is therefore not concerned with biologically unrealistic models used in connectionism, machine learning, artificial neural networks, artificial intelligence and computational learning theory.
Models in computational neuroscience are aimed at capturing the essential features of the biological system at multiple spatial-temporal scales, from membrane currents, and chemical coupling via network oscillations, columnar and topographic architecture, all the way up to memory, learning and behavior. These computational models frame hypotheses that can be directly tested by biological or psychological experiments.