No way.
From what I've understood, we are so far from understanding even how a single neuron works. There's still plenty of debate about that.
From what I've understood, we are so far from understanding even how a single neuron works. There's still plenty of debate about that.
At one time, people had no idea how the brain worked at all. Now we know that the brain is a collection of neurons, special cells which have filaments called an axon and dendrites that extend from it to other neurons at junctions we call synapses. At these synapses, electrochemical signals are transmitted to and from other neurons. When a neuron receives certain signals, it may transmit that signal on to other neurons. That's basically the Anatomy 101 chapter on the nervous system, and has been pretty well established for about a century.
It's true that we're constantly learning new things about diseases that affect the function of the nervous system. There remains some debate on how exactly neurons arrange themselves, repair themselves, and change. We're still investigating internal computations within the dendrites to see how those work to decide when to propagate a signal or not. The signals are analog in some ways, but mostly digital 'all or nothing' responses, there's time coding, different types of discharge patterns...fractal complexity.
If you set the goalposts at a comprehensive understanding of every bit of trivial minutiae that exists in the human body, yeah, we are (and probably will always be) far off. If you just want to predict the function of a nematode worm with 302 neurons, observing that if you engage this particular set of sensory neurons, then these few hundred synapses are activated with this pattern of impulses, and soon those other motor neurons are stimulated and the worm curls in that direction...that seems entirely within the realm of possibility. In my opinion, the goalposts ought to be "A better understanding than what we have today". We'll reach them by tomorrow, and move them forward again!
The goal here seems to be to start by copying it without understanding in order to gain understanding. AFAIK we already simulate brains of silk worms or some such thing?
There is research into mapping the complete neural structure of a nematode worm called C. elegans [0] - one of the simplest known organisms that has some kind of a nervous system (the entire worm has 959 cells for hermaphrodite individuals, or 1035 for males; of these, 302 are neurons - and yes, this is not a typo, these are not hundreds or thousands of cells, just cells - that's how tiny it is). Its entire nervous system has been mapped out, including all connections, in [0]. Note that its neural cells are not like mammal neurons, they lack many of the known processes present in even a single mammal neuron.
However, the project to simulate this simplest of organisms, OpenWorm [1], has not yet achieved its goals (which is also somewhat reduced - they only intend to simulate the motor system initially, not the entire organism).
Silkworms (which are moths) are waaaay beyond any imaginable simulation capability at the moment.
The paper is talking about taking another approach. Whether it will work or not is not something I could comment on.
Another example is how we have a very imperfect model for the universe, and yet we use our models to make strikingly accurate predictions. Huge gaps in our knowledge exist, but that doesn't stop us from using approximations to do interesting things.
But that's just it - first they spent some time understanding how the system works,painstakingly mapping out each connection, the role of each individual cell (literally), they studied the chemistry inside each neural cell etc. The simulation effort started after all of this was done.
If anything, I would say OpenWorm is a counter-example to the idea that you can simulate such a system without understanding it.
Similarly, with our model of the universe, while gaps exist, the extent of those gaps and approximations is mostly known, and the areas affected by those gaps are exactly where we can't make predictions (e.g. the internal structure of a black hole). Not to mention, we had a massive piece of good luck there: it turns out that particles are described by linear equations, which have extremely nice properties for approximations. Already with GR we are in much worse territory with it's nonlinear equations, but at least we understand these well enough to create some linear approximations - or so we think.
For example: do we need to simulate all the DNA transcription to make a virtual neuron? Currently this is unknown. And if it is required it's pretty much game over already.
I don't believe you need that level of fidelity but that's more of a hope than a scientific standpoint.
And often the fastest and most economical way to find out is to try doing it.
Also simulations need not be perfect to be useful. I spent a large fraction of my life writing object oriented simulations of transformers. We simplified everything (reduced detail, reduced number of dimensions, rules of thumb where there was no analytical formula) but still succeeded in designing better transformers than before.
The next generation will be more precise and even more useful but you can't always get there in one jump.
I'm pretty sure OpenWorm has not achieved its goal.
My take on this would be, sure, copying it is an important step, but they don't know to copy it, so it won't work.
Effort/interest is not really fungible or spendable like this. People work and study on the things that are interesting to them. We can't just flip a switch to get everyone who's researching brains to transition to researching space travel.
Given that "sufficient impact", then the "system understanding itself" issue arises.
However, if you believe the neuron is the same as the neuron in the worm, then the concept of how it works should (by my best guess and nothing else) be adequately simple for human understanding.