Not necessarily. Everything points to the fact that the humain brain (at least, I don't know much about ants) does not work in any way similar to neural networks, and as such there's no guarantee that you can represent its behaviour by the current algorithms.
For example, real neurons have no supervision signal. Memory is also a big issue (see the work being done by DeepMind and FAIR on differentiable neural computers, and memory networks) and Reinforcement Learning still struggles with long-term planing, switching strategies, etc.
Yes, the brain is most likely not simply modelling one giant ANN, but it's much more plausible that ANN-like components have a role in it.
Doesn't the universal function approximation theorem provide just such a guarantee? It doesn't guarantee any algorithm will converge on that representation, but the capability to represent it is there.
An ant’s fitness function is different from its ancestor’s fitness function, which is different from its own ancestor’s fitness function, which is different from...
You don’t get from zero to complexity with a single fitness function.
The question we should ask is: How do we vary the fitness function over time in order to evolve something complex?
I have no idea what the answer is, but...
“If you know the question, you know half.”
-Herb Boyer, geneticist
The question is, what is that function?