  The mathematical model underlying the Neural Engineering Framework (NEF)
expresses neuronal input as a linear combination of synaptic currents. However,
in biology, synapses are not perfect current sources and are thus nonlinear.
Detailed synapse models are based on channel conductances instead of currents,
which require independent handling of excitatory and inhibitory synapses. This,
in particular, significantly affects the influence of inhibitory signals on the
neuronal dynamics. In this technical report we first summarize the relevant
portions of the NEF and conductance-based synapse models. We then discuss a
na\"ive translation between populations of LIF neurons with current- and
conductance-based synapses based on an estimation of an average membrane
potential. Experiments show that this simple approach works relatively well for
feed-forward communication channels, yet performance degrades for NEF networks
describing more complex dynamics, such as integration.
