No. No one has simulated a human brain. All those projects have simulated something that might vaguely resemble some of the brain's structures or operations, but we know so little about the whole thing that it was pretty much a pointless exercise.
HBP has been different from all of them because they attempted to do the true bottom up approach, not feasible without massive resources. As the article states, it turned out that even given the massive resources the task is just too difficult currently.
My day job is building and evaluating such models; we have one on seizure propagation that is entering clinical trial.
The only "model of a whole brain" we have currently is the one of C. elegans, and even that's debatable because it does not model chemical processes which modulate neural activities, and therefore does not provide an accurate input/output mapping (in general).
No, it doesn't predict activity from parameters.
> C. elegans, and even that's debatable because it does not model chemical processes
"the map is not the territory". This sort of criticism isn't even scientific
Not sure what you mean.
For example, we have a whole brain model of rest state dynamics that can represent aspects of data (eg graph metrics such as degree) from multiple sclerosis patients, distinctly from healthy controls. Here, the map is the model parameters, and the territory is the patient's actual brain. The map is deemed useful if its structure represents that of the territory.
http://www.scientificamerican.com/article/the-root-of-though...
AFAIK, nobody has ever claimed to be trying to produce a human whole-brain emulation in the style of https://neurokernel.github.io/. It's a problem that is obviously intractible right now.
I don't think anyone playing SimCity thinks there's a whole real city running on their CPU. And certainly no one has a heart attack if you say SimCity is a simulation of a whole city.
We need to have some standard, and I would say the bare minimum is modeling each neuron in an ultra-simple way along with the connections between them.
Great question (to which I have no idea what the answer is). It seems to me that the various attempts are trying to simulate sets of emergent behaviours at different levels of the brain's "stack". I guess, one way of determining the success of a "lower level" simulation/emulation, is the emergence of known "higher level" behaviours?
Their website is https://nengo.ai and I am not affiliated with them just an alum that took his course.
Human brain is a very specific and complex brain which we do not understand - vastly less than even cities at any scale. And to produce any useful answers, the model has to be accurate to some degree - and we don't know how much. HBP indeed had no question which it tried to answer - it's more like climbing a mountain because it exists.
Pretty much every researcher wants money to fund ambitious projects, but you can't just go around making all kinds of nonsense claims without being able to back them up. The HBP has produced some good research, but for the amount of funding they've received I would have expected much, much more.
Ultimately I think the fault lies within the funding structure. So much of scientific funding incentivizes bold and brash statements and plans, but very little consideration is given towards having the technical ability to execute those plans.
For a project of this size and scope, I would have wanted to see significant investments in computational infrastructure and software/hardware development very early on. At least the investment would have yielded something to build upon. Imagine if the Apollo mission tried to go to the moon with parts built from a Yugo. It wouldn't matter how many smart mathematicians you hire, or how skilled your navigators are, you just aren't going to get to the moon. Unfortunately computational infrastructure just isn't sexy enough to invest in, and I would say as a whole (there are some exceptions), neuroscience leadership just does not have the technical expertise to handle these kinds of projects yet.
Still I agree infrastructure is a really hard problem, which is not going to get solved anytime soon. Just as an example, federated identity across HPC resources isn't even on the table, even though they are working on the next funding iteration. This means you need 3+ accounts to get anything done in the HBP way.
I don't mean to be petty, but I read that with a very intellectually dishonest tone: By misleading the goals, we secured funds to do what we really intended which wasn't sexy enough:
Thats grounds to call in the research audit and close things down.
> In 2013, the European Commission awarded his initiative—the Human Brain Project (HBP)—a staggering 1 billion euro grant (worth about $1.42 billion at the time).
10yr run ways and massive amounts of capital sounds like the perfect recipe for black hole a money pit.
I'd rather leave 'innovation' to academia and private industry. If they want to help then make those two things easier, don't try to do it yourself (like trying to pick the winners).
That is not to say that the HBP wasn't widely perceived as ridiculous from the outset by people with relevant technical knowledge. I think the bottom line is that we should respect scientists that are able to cast an audacious vision, raise crazy amounts of money, and then actually deliver, e.g., Christoph Koch/Allen Institute.
https://www.washingtonpost.com/news/answer-sheet/wp/2018/06/...
1) https://www.reddit.com/r/chemistry/comments/cgy6uz/comment/e...
I left academia shortly after that (no interest in that competition) for FAANG and got more and better research done in my 20% time than I ever did in academia. I've spoken to many program managers who would love to fdund clever new ideas from creative young PIs, but are under strain to produce a reliable stream of papers from more experienced PIs who train postdocs.
What kind of neuronal / plasticity models do these chips implement? If they are anything like what others are doing with integrate and fire neurons, these do not seem to align with the goals of the project. You need detailed, compartmental biophusical simulations and not abstracted, approximate models of neurons. Otherwise, we are already able to run such kinds of models with a large cluster.
Several options available but followings are superior to others in terms of capturing the real neuron/synapse behavior with a simple and compact VLSI implementation.
For neurons: Exponential adaptive integrate and fire models @ real-time.
For synapses: STD/STP + LTP/LTD circuits @ real-time.
Nope, you can keep going with the same logic: HH is just an approximatiin of the molecular kinetics and what you really need are the FEM models in 3D with all the protein pathways etc etc.
But this leads to an entirely intractable project. The science lies not in reproducing the exact neurons bug for bug, but keeping only the necessary details, within technical possibility, for explaining some observed phenomenon. LIF and AdEx neurons seem like a good compromise for neuromorphic hardware.
everyone draws the line somewhere, this is yours. Even we take this statement to be true, you still have metabolic networks changing transmitter concentrations, dendritic arbors evolving in entirely unidentifiable ways, etc. The goal of modeling is not to say that every detail is there, but ones relevant to account for specific feature of data.
I've been wondering if dendritic arborization means that current deep learning ANNs are hopelessly far away from biological reality, or if perhaps with deep networks simple ANNs could indeed learn to compute in a similar way to complex biological neurons, just over many layers of artificial neurons.
1 https://www.sciencedirect.com/science/article/pii/S089662730...
edit despite working within the project I definitely think a critical view of such large funding is essential, so articles like this are welcome