101–110 of 112 posts
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> 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.
Maybe making human brain actually crave such activities would be even more... valuable.
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Everybody in the history of humans has said the latest technology is the best model for how a brain works. There used to be a piston model for the brain. Self driving cars can't leave an enclosed environment and might never do so safely. Richard Dawkins spoke very highly of the brains ability to do some kind of natural calculus for the sake of tracking a ball in flight, but most animals run on simple tricks and refer…
> Everybody in the history of humans has said the latest technology is the best model for how a brain works. There used to be a piston model for the brain. Is this the same mistake as in "The Relativity of Wrong" [1]? > people have thought they understood the Universe at last, and in every century they were proven to be wrong. It follows that the one thing we can say about out modern "knowledge" is that it is wrong.…
Ancients used to think that thinking happened in the gut and recently the microbiome pathway for describing thought has re-emerged. Both the gut pathway and the computational stream could be wrong.
The outcome of seeing the brain as a computational device will run out of juice like revelation has.
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 behavior…
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?
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 behavior…
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…
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.
Earlier quoted context omitted.
> 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.
Maybe making human brain actually crave such activities would be even more... valuable.
Earlier quoted context omitted.
> Aside: I'm surprised that the FAANGs haven't revolutionized statistics yet. What does that even mean?
Like, with QM, we had the time and equipment to start pointing radium at some gold foil. We came up with some surprising results. With economics, we finally got enough data in to say that people really are not rational at all. With biology, we finally got the time and equipment to poke audio amplifiers into rabbit brains and some strange stuff happened. Etc. Similarly, with all this 'big data', I would have guessed t…
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How do you validate your models if you can't validate a simpler one first?
c elegans has very primitive, simple behaviours. It's not really possible to get something useful out of it about either our cognitive functions or our brain disorders. The things that regard single cell pathologies (e.g. plasticity) are already studied in vitro in mammalian cells. There s probably many cognitive phenomena that only become apparent in large brain sizes, so i m not sure this method scales up.
What would you recommennd?
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 behavior…
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…
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.
Earlier quoted context omitted.
c elegans has very primitive, simple behaviours. It's not really possible to get something useful out of it about either our cognitive functions or our brain disorders. The things that regard single cell pathologies (e.g. plasticity) are already studied in vitro in mammalian cells. There s probably many cognitive phenomena that only become apparent in large brain sizes, so i m not sure this method scales up.
In what situation WOULD you be able to expect to extract "something useful" about "our cognitive fucntions or our brain disorders"? It seems silly to think we could learn anything about such complex things without understanding something simpler first, hence the approach of validating models of simpler structures. What would you recommennd?
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 behavior…