Abstract: Neurons in the brain are not identical: they show a wide variety of properties. Recently, we showed that this neural diversity itself is not static: it depends on for instance input characteristics and on neuromodulatory state. How does this heterogeneity influence the dynamics and information processing of neural networks? Is this a dial the brain can use to adapt to the requirements of different tasks? I will discuss our latest analysis of experimental data on the heterogeneity of cell properties and how this influences network dynamics and performance in different tasks using different types of network simulations.