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Summary: The dendritic tree of neurons plays an important role in information processing in the brain. While it is thought that dendrites require independent subunits to perform most of their computations, it is still not understood how they compartmentalize into functional subunits. Here, we show how these subunits can be deduced from the properties of dendrites. We devised a formalism that links the dendritic arborization to an impedance-based tree graph and show how the topology of this graph reveals independent subunits. This analysis reveals that cooperativity between synapses decreases slowly with increasing electrical separation and thus that few independent subunits coexist. We nevertheless find that balanced inputs or shunting inhibition can modify this topology and increase the number and size of the subunits in a context-dependent manner. We also find that this dynamic recompartmentalization can enable branch-specific learning of stimulus features. Analysis of dendritic patch-clamp recording experiments confirmed our theoretical predictions. : Many neuronal computations rely on the compartmentalization of dendrites into functional subunits. Wybo et al. investigate the number of these subunits that can coexist on a neuron. They find that there are substantially fewer subunits than dendritic branches but that subunit extent and number can be modified by synaptic inputs. Keywords: neural computation, dendrites, compartmentalization, independent subunits, branch-specific learning, dendritic computation