[ImaTigger]

I am a computational cognitive neuroscientist, an have worked at many levels. I find each kind of data and model useful to some extent, but I have to admit that the least useful, are, to my mind, those at the detailed neural network level, like the ones discussing in this paper. Somewhat more useful are higher level dynamic architecture models, and, at the highest level, cognitive models, which constrain the behavioral target we are trying to explain. I personally (as one can tell from my other posts here) find the dynamics brain development models to be the most compelling as overall models, but they are not particularly explanatory at the detailed level. Brain science is trying to do the hardest thing you can imagine, that is, explain the most complex machine in the known universe. We persist, but no one entering this field should have very high expectations of near term grand successes.

[lambdaloop]

As a counterpoint, I am a computational neuroscientist who transitioned form working in human cognition to fruit fly motor control. Fruit fly neuroscience in the past decade has advanced tremendously. With the latest tools, we can record activity from specific genetically labeled neurons while stimulating others. We have identified specific groups of neurons to stimulate to get the fly to groom, walk, turn, and even walk backwards. The full fly brain has been scanned with similar techniques and the connectome is beginning to be mapped out (e.g. see this very recent post from Google AI research https://ai.googleblog.com/2020/01/releasing-drosophila-hemib... ).

I find that as we gain new tools to study the nervous system more specifically, both data and models of how neurons are organized at the circuit level become more important. To advance on an analogy in the article, it's like trying to explore the dynamics of NYC without a map. For instance, it's hard to tell how/why people interact with central park if you don't even know where they live. The more specifically you are able to pin down people, the more it matters where exactly they live to understand.

Granted, the fly is much simpler than humans or even mice, and it will likely take decades and new tools for us to study humans in this way. However, when we get there, mapping out the brain connections will be crucial to make sense of it all.

[0239kffkk]

To me, though, a better analogy is assuming that because one has a detailed map of the sewer system of NYC, we now understand where we're going in Berlin, or Barcelona, or Vancouver, or that that level of detail is necessary to understand the economics of poverty or pollution. That wouldn't work for city planning and I don't know why people assumes it works for neural architecture either.

Similar tricks can be played with the human brain, things we have been able to do for decades, while people are undergoing brain surgery, and now later, with TMS. However, being able to elicit limb movements or bits of speech, or even emotional qualia is different from having a dynamic understanding of the brain in vivo in everyday life.

Certainly having an understanding of detailed circuitry is interesting and important, but to me there's a forest for the trees problem.

[Joof]

The poster above you is why I decided to just work as a software engineer instead. It was such a depressing and bleak field. Thank you for having another opinion!

[chromanoid]

To me all these mapping and monitoring efforts always seem like trying to reverse engineer Microsoft Word's grammar checker by measuring electrical signals on various parts of a computer that somehow got into the 18th century.

I really hope we can realize how the whole thing works by looking at its parts. But I doubt it will bring the breakthrough. On the other hand maybe there is this one mechanism that we have to discover to make sense of all the parts. Then those efforts will form the groundwork for an explosive understanding.

[gedy]

> We have identified specific groups of neurons to stimulate to get the fly to groom, walk, turn, and even walk backwards

This made me realize we may one day willing allow human brain controls to get us to do things we don't want - Work, Exercise, etc.

[erikpukinskis]

> human brain controls to get us to do things we don't want - Work

We already have that: coffee.

[nine_k]

Maybe making human brain actually crave such activities would be even more... valuable.

[asdff]

Far easier to dump meth in the water cooler

[ggm]

much simpler by how many orders of magnitude? If it's surface area driven it rises as a function of size more slowly for increases in volume. If it's connectionist then it's an O(n^2) Increase?

[0239kffkk]

I had a similar reaction, working in an adjunct field but as someone who often works with neuroscientists.

My impression is that there's a lot of very oversimplified assumptions being made all the time in these fields that get glossed over in very arrogant (or naive?) ways. It's really astonishing to me, not just because of how oversimplified the assumptions are but because researchers are then surprised things don't work out.

To be fair, this is true of other fields as well. I'm more familiar with molecular genetics and genomics, and the same things happen there. There seems to be a certain hubris that goes unquestioned, and it always amazes me, the sci-fi fantasy narrative being accepted as fact.

Just to take one thing for example: there's huge anatomical differences between people's brains even at the macroscopic level, that just get glossed over in discussion. Those fMRI images you see? They're often done by aligning different images to a common map, just assuming individuals' brains are carbon copies of one another. Now you're going to try to delineate a connectome at the neural level, as if there is one connectome at that level?

When will everyone learn? Where's the public skepticism?

[lr4444lr]

Thanks. Comments like this from people with a long and broad experience in a field are why I keep coming back to HN, because it helps me know where to look next if I want to verify or learn more about a new topic. What do you think we learn should keep our eye on specifically for new developments?