It's more bidirectional than that. Higher-order cognition (language, memory, even perception and attention) isn't so easily reducible. Take for instance the hippocampus. Sure, we can simulate circuitry with precision but that doesn't explain memory formation and retrieval. More "artificial" approaches can help to explain systems from the top down even as biological constraints are more rigid from the bottom up. Both modeling approaches are likely to meet somewhere in the middle. The computational shortcuts in the more abstract models (e.g. backprop) are really just a shorthand to allow investigators to focus on less biologically-driven details or those that are not now understood in biological terms.
For instance, I know of one group using analytic techniques from social networks to correlate brain regions in fMRI data. Is the brain a massive social network? I don't think any one would say that literally. But right now, that approach is as good as any other to examine n-dimensional relationships in highly complex data.
This discussion reminds me of Paul Krugman's argument for cartoon models. I personally think that we can isolate, and therefore explain, simple parts of aggregate neural behavior by artificial construction more easily than we can by doing careful biology.
Incidentally, you might be interested to know that restricted boltzmann machines are much more biologically plausible than backprop, and seem to work faster and better.
For instance, I know of one group using analytic techniques from social networks to correlate brain regions in fMRI data. Is the brain a massive social network? I don't think any one would say that literally. But right now, that approach is as good as any other to examine n-dimensional relationships in highly complex data.