We present a simple method for the realistic description of neurons that is well suited to the development of large-scale neuronal network models where the interactions within and between neural circuits are the object of study rather than the details of dendritic signal propagation in individual cells. Referred to as the composite approach, it combines in a one-compartment model elements of both the leaky integrator cell and the conductance-based formalism of Hodgkin and Huxley (1952). Composite models treat the cell membrane as an equivalent circuit that contains ligand-gated synaptic, voltage-gated, and voltage- and concentration-dependent conductances. The time dependences of these various conductances are assumed to correlate with their spatial locations in the real cell. Thus, when viewed from the soma, ligand-gated synaptic and other dendritically located conductances can be modeled as either single alpha or double exponential functions of time, whereas, with the exception of discharge-related conductances, somatic and proximal dendritic conductances can be well approximated by simple current-voltage relationships. As an example of the composite approach to neuronal modeling we describe a composite model of a cerebellar Purkinje neuron.