Fwiw I am involved in an ongoing project that is investigating a biologically plausible model for generating connectomes (as neuroscientists like to call them). The connectome-generator happens (coincidentally) to be a neural network. But exactly as the OP points out, this "neural network" need not actually represent a biological brain -- in our case it's actually a hypernetwork representing the process of gene expression, which in turn generates the biological network. Backprop is then applied to this hypernetwork as a (more efficient) proxy for evolution. In the most extreme case there need not be any learning at all at the level of an individual organism. You can see this as the ultimate end-point of so-called Baldwinian evolution, which is the hypothesized process whereby more and more of the statistics of a task are "pulled back" into genetically encoded priors over time.
But for me the more interesting question is how to approach the information flow from tasks (or 'fitness') to brains to genes on successively longer time scales. Can that be done with information theory, or perhaps with some generalization of it? I also think it is a rich and interesting challenge to parameterize learning rules in such a way that evolution (or even random search) can efficiently find good ones for rapid learning of specific kinds of task. My gut feeling is that biological intelligence has many components that are ultimately discrete computations, and we'll discover that those are reachable by random search if we can just get the substrate right, and in fact this is how evolution has often done it -- shades of Gould and Eldredge's "punctuated equilibrium".
(if anyone is interested in discussing any of these things feel free to drop me an email)