You have to start somewhere though.
Part of the issue is semantics, what do you consider intelligent? And part is empirical. Insects pop out of a an egg and start doing their 'thing.' Some argue its "intelligence" and others argue is programming of an automaton. Having seen a number of mice go through the whole cycle, from birth to snake chow, my observation was that capabilities developed over time but intelligence appeared to be innate. (which is different than learning, it is in my opinion the ability to learn). I don't know how one my structure an experiment of keeping a mouse effectively in a coma until its body was fully grown, and then measuring its ability to learn, but I did observe that baby mice learned readily things like how to avoid losing track of mom, and how to keep warm. Programming or instinct or something else I don't know.
I do agree that without inputs the brain does not learn but I would not agree that it is not intelligent. So the question might be how fast can it learn. An interesting question might be is it possible to measure the speed of learning independent of a mouse acting on that learning.
my -> might.
I think I've worked on too many large software systems, but my guess?
The human brain is the world's worst spaghetti code and this will be impossible.
The fact you wrote this sentence, let me assume your prefrontal cortex has actually some more abilities :)
The purpose of a brain is to control homeostasis, motor, and perception functionality. Without a rich world simulation, the brain is just an isolated processor. By modelling at the neuron level, the only inputs can be at the neuron level. That means you have to physically model the entire mouse's body to connect all the inputs.
Even if you had a realistic body simulation, you also need to run the system for an extended period of time. Intelligence is an emergent behavior, and it needs time to work and learn. Since you have to model every single neuron firing at the same time, the speed at which you can run this model is unreasonably slow.
I feel like approaches which slowly build a system of models, each of which simplifies the function of a particular neural region to something computationally tractable is the more realistic approach. In doing so, you develop models which reveal the structure of intelligence.