Additional simulation results.

<div> Synaptic plasticity is essential for memory formation and learning in the brain. In addition, recent results indicate that non-synaptic plasticity processes such as the regulation of neural membrane properties contribute to memory formation, its functional role in memory and learning has however remained elusive. Here, we propose that non-synaptic and synaptic plasticity are both essential components to enable memory-dependent processing in neuronal networks. While the former acts on a fast timescale for rapid information storage, the latter shapes network processing on a slower timescale to harness this memory as a functional component. We analyse this concept in a network model where pyramidal neurons regulate their apical trunk excitability in a Hebbian manner. We find that local synaptic plasticity rules can be derived for this model and show that the interplay between this synaptic plasticity and the non-synaptic trunk plasticity enables the model to successfully accommodate memory-dependent processing capabilities in a number of tasks, ranging from simple memory tests to question answering. </div>