There is a long-standing debate in theoretical neuroscience about what information of spikes is most important: rate or phase? What stance does the NEF take on this manner?
The NEF has no formal stance on whether rate information or phase information is of importance. All it cares about is spikes.
As proof, consider the derivation of the decoders, which is the core of the NEF.
The Decoders, $d$ are a vector of synaptic weights applied to the activities of neurons (one decoding weight per neuron) to approximate a given function $f(x(t))$. They can be thought of as the axons of a neuron.
Given the spiking neurons activities in the matrix $A$ (rows are neurons, columns are evaluation points), to an input $x$, this relation can be explained as:
$$A \cdot d = f(x(t))$$
Given an equation with one unknown, where a matrix $A$ is combined with a vector $d$ to get another vector $f(x(t))$, this can be solved using linear algebra to find $d$.
Note how there is no mention of rate or phase in this derivation. Admittedly, in the Nengo neural simulator, rate neurons can be used and functions that take phase into account can be calculated, but these design choices are up to the modeller and are not forced by the NEF itself.