I think the same is true of simulation and modeling: it requires a domain expert to include the useful stuff in your model while skirting or abstracting away the extraneous bits.
That's the point!
Knowing what is big and what is small is more important than being able to solve partial differential equations.
The trick is that the map itself is 1:1 but your moving around in the map is not constrained by the same limitations as in the real world.
Whoever came up with that saying just wasn't very imaginative.
0. https://en.wikipedia.org/wiki/Exercise_Tiger
1. https://www.independent.co.uk/news/uk/this-britain/welcome-t...
A model is always an incomplete simulation of something, keeping some relevant aspect but discarding less relevant ones while remaining useful.
Suppose one day we manage to create brain uploads. They're not brains but would have additional or different capabilities despite being 1:1 maps - being possible to restore the original from them. This is because they could be ran on different hardware.
Likewise, consider lossless compression. It is an accurate model of the data in shorter form.
Or just call it a simplification instead of a model. It's a subset of all models. Alternatively, an explanation of it captures all the detail.
Model is combined with another interesting word, essence as in essential, which is what it's supposed to capture, for given purpose.
If the purpose of the model is explanatory power and accuracy, these two do not necessarily form a one dimensional trade-off.
That's because language is mostly useful for communication between people, here and now. Treating a natural language as a construct to refine until it reaches a mathematical beauty where nice and short words mean what they "should mean" often causes so much grief and wasted energy.
A braindump would keep some aspects, like the neuron configuration etc, but discard others, like vascular properties, exact positional information of neurons etc. You could even go as deep as exact positioning of all contained atoms in the original brain.