  Neurons are spatially extended structures that receive and process inputs on
their dendrites. It is generally accepted that neuronal computations arise from
the active integration of synaptic inputs along a dendrite between the input
location and the location of spike generation in the axon initial segment.
However, many application such as simulations of brain networks, use
point-neurons --neurons without a morphological component-- as computational
units to keep the conceptual complexity and computational costs low.
Inevitably, these applications thus omit a fundamental property of neuronal
computation. In this work, we present an approach to model an artificial
synapse that mimics dendritic processing without the need to explicitly
simulate dendritic dynamics. The model synapse employs an analytic solution for
the cable equation to compute the neuron's membrane potential following
dendritic inputs. Green's function formalism is used to derive the closed
version of the cable equation. We show that by using this synapse model,
point-neurons can achieve results that were previously limited to the realms of
multi-compartmental models. Moreover, a computational advantage is achieved
when only a small number of simulated synapses impinge on a morphologically
elaborate neuron. Opportunities and limitations are discussed.
