Effects of passive dendritic arborization on neuronal response in extended integrate and fire models

The shape and morphology of dendritic trees suggest that these structures are actively involved in processing the information they receive, enabling neurons to perform a large variety of computations previously believed to be possible only at a network level. The ability of dendrites to perform computations is believed to emerge in the presence of active conductances which can boost or suppress the effect of incoming input in a non-linear fashion. The secrets of dendritic computations are believed to be hidden in these non-linearities. However, it is still unknown whether the passive properties of dendrites alone play a role in their formation. In this work, we will focus on a reduced model, a simplified Leaky Integrate-and-Fire (LIF) somatic compartment equipped with passive dendritic processes. Our aim is to investigate whether a ramified dendritic tree, in the absence of active conductances, is able to produce different responses to the same patterns of stimulation with respect to an equivalent cylinder displaying the same electrical properties. The results of this work may shed light on the functional role of ramification in dendritic information processing.