  Nonlinear interactions in the dendritic tree play a key role in neural
computation. Nevertheless, modeling frameworks aimed at the construction of
large-scale, functional spiking neural networks, such as the Neural Engineering
Framework, tend to assume a linear superposition of post-synaptic currents. In
this paper, we present a series of extensions to the Neural Engineering
Framework that facilitate the construction of networks incorporating Dale's
principle and nonlinear conductance-based synapses. We apply these extensions
to a two-compartment LIF neuron that can be seen as a simple model of passive
dendritic computation. We show that it is possible to incorporate neuron models
with input-dependent nonlinearities into the Neural Engineering Framework
without compromising high-level function and that nonlinear post-synaptic
currents can be systematically exploited to compute a wide variety of
multivariate, bandlimited functions, including the Euclidean norm, controlled
shunting, and non-negative multiplication. By avoiding an additional source of
spike noise, the function-approximation accuracy of a single layer of
two-compartment LIF neurons is on a par with or even surpasses that of
two-layer spiking neural networks up to a certain target function bandwidth.
