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An Existential Crisis in Neuroscience
- HenryKissinger 7y ago"If the human brain were so simple that we could understand it, we would be so simple that we couldn't."
- mannykannot 7y agoThat is a cute aphorism, but there is no empirical evidence one way or the other. If you are thinking that it is obviously too much information for a brain to know itself, note that most of what we understand about complex systems, such as the weather, is understood using only a miniscule fraction of the total information present. That is the power of abstraction.
- rusk 7y agoA mind understanding itself may violate the second law, but a few minds understanding one mind should be theoretically possible!
- notahacker 7y agoThe organization of human thought to achieve that level of understanding might be an issue. From the original article: 'the data footprint of all books ever written come out to less than 100 terabytes, or 0.005 percent of a mouse brain'
- rusk 7y agoThat formulation makes the goal seem remote but not implausible. We have the technology to index and analyse 10 Terrabytes certainly and even gmail apparently is an exabyte ... [0]. A mouse brain given your premise is 20 petabytes. Given even linear progress this class of problem is not insurmountable. [0] https://en.m.wikipedia.org/wiki/Exa- https://en.m.wikipedia.org/wiki/Exa-
- soup10 7y agoIf you've seen some of the high resolution videos of neural activity captures from even simple fish, the slightest motor movements activate hundreds of thousands of cells in a chaotic pattern. Neural circuitry is not neatly laid out like a silicon chip, its a forest of inter-connectivity that resists analysis even with extremely detailed visualization and data captures.
- sorokod 7y agoIf a highly structured action seems to be driven by system whose visualisation looks chaotic, perhaps we are using the wrong visualisation.
- the-dude 7y agoMaybe it is DRM
- dr_dshiv 7y agoI love those videos. We will be getting a lot more, soon, as well with the new voltage-sentitive microscopy. I think the best story for understanding circuitry in vertebrates comes from the work on hierarchical pattern generators -- where much of the work was done on lampreys. Grillner, S. (2006). Biological pattern generation: the cellular and computational logic of networks in motion. Neuron, 52(5), 751-766.
- erikpukinskis 7y agoI think it’s interesting that people think we’ll be able to make something that does more in a smaller space. As if there was something other than the laws of physics preventing natural selection from testing smaller structures. Or that there’s something (other than the demands of the computation itself) constraining the architectures that were tested through natural selection.
- whatshisface 7y agoConstraints: - No temperatures hot enough to melt silicon or metal. - Everything must unfold from a single cell during the process of reproduction, no factories allowed. - Everything must be made out of what it can find to eat, no mining allowed unless you're a subterranean bacteria.
- dr_dshiv 7y agoWhat we need is a Newtonian model of the brain. A model that is incomplete and "wrong", but useful and generative. While Newtonian physics may be "wrong", is much easier to learn than quantum physics or relativity, etc. Neuroscience usually focuses on precision details, but doesn't aim to tell big picture stories. There are a few exceptions, however, like Karl Friston's free energy reduction model.
- Waterluvian 7y agoDon't we have that in the model that describes what the parts of the brain do? Proven to be wrong when you watch people with serious brain injuries re learn skills, but shown to have value by how it can predict what tumors or injuries will do to someone's ability.
- jagger27 7y agoI would equate our current understanding of the brain to the common knowledge that apples fall to the ground from trees; where else would they go? People knew that long before Newton.
- im3w1l 7y agoAlmost 2000 years before Newton, Archimedes wrote "Any object, totally or partially immersed in a fluid or liquid, is buoyed up by a force equal to the weight of the fluid displaced by the object." The ancients knew a fair bit.
- dr_dshiv 7y agoI would argue not since it doesn't do much for explaining how it works. We know how computers process information. We don't really have a good story for how the brain does it. We need a story that explains how the brain uses rhythms (oscillations) for computation.
- numpad0 7y agoWe know how to wire FETs, we know this is an i5 and is fast, we also know how to solder thick wires or how to loosen large screws, but no one knows how to build even a Z80 out of some germanium is how neuroscience is understood in my understanding
- brian_spiering 7y agoThere is a good chance that complete brain mapping will be similar to whole genome sequencing. The result will be interesting but only answer a limited subset of questions.
- htfu 7y agoGenerally too purple for its own good but a very interesting read! The Borges reference (and C. elegans conundrum) makes me as a layreader really appreciate how little we actually know about the endgame for all this data. But there is such elegance in "rudimentary" DNN's giving us the ability to at all assemble this stuff.
- gbjw 7y agoTangentially related: Could a Neuroscientist Understand a Microprocessor? [1] (short answer: not with current analytic tools) [1] https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005268 https://journals.plos.org/ploscompbiol/article?id=10.1371/jo...
- lonelappde 7y agoThat's a bit silly because neuroscience tools are built got biological brains. electrical engineering tools are used on microprocessors, as they should.
- jvanderbot 7y agoOP and TFA meant "Would a similar method of observing a complex, connected system of systems work on a less complex example like a microchip if we had just up and found one?"
- SubiculumCode 7y agoThis is an over-rated article by those outside the field of neuroscience. While it brings up a few good points, it fails to acknowledge the sophistication of our neuroscience and behavioral techniques, and the value of converging evidence across levels of description. Also, its relatedness to the present article is tenuous at best.
- SubiculumCode 7y agoI don't care how often I get down voted for making the above comment in response to posts about this article. I am a neuroscientist and I will defend my field from overrated, simplistic criticisms that happen to appeal to the HN crowd's sensibilities.
- hsitz 7y agoDo you have any suggestions for what the average layperson could read to get a better understanding of contemporary neuroscience? Any articles or books you'd recommend?
- erikerikson 7y agoWe'll simplify and create models, as we do in maps and all else, identifying principles and prioritizing.
- pfdietz 7y agoI think they need to fully figure out how individual neurons work before they move up to larger circuits.
- _xerxes_ 7y agoThe article touched upon the C. elegans connectome. There are a few interesting projects attempting to simulate the creature. https://en.wikipedia.org/wiki/OpenWorm https://en.wikipedia.org/wiki/OpenWorm https://en.wikipedia.org/wiki/WormBase https://en.wikipedia.org/wiki/WormBase
- buboard 7y agotbf it's debatable if there 's a lot to learn from C.elegans. Simple animals have been studied for decades from aplysia to the mouse. But those are not behaviours that are interesting when attempting to learn more about the human brain. The Allen institute's connectome project is more relevant to mammals, even if it's only a tiny volume of the mouse cortex, in order to mildly constrain models of brain function. Even if we had the whole brain, it s too large to be simulatable. These data help our understanding, and we 're lucky we have amazing tools to probe brains at this moment. But we need more and better theories to put them to good use
- monadic2 7y agoHow do you validate your models if you can't validate a simpler one first?
- buboard 7y agoc 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.
- monadic2 7y agoIn 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?
- SubiculumCode 7y agoFrom the article, a quote that is enlightening: if I asked, ‘Do you understand New York City?’ you would probably respond, ‘What do you mean?’ There’s all this complexity. If you can’t understand New York City, it’s not because you can’t get access to the data. It’s just there’s so much going on at the same time. That’s what a human brain is. It’s millions of things happening simultaneously among different types of cells, neuromodulators, genetic components, things from the outside. There’s no point when you can suddenly say, ‘I now understand the brain,’ just as you wouldn’t say, ‘I now get New York City.’ ”
- randcraw 7y agoTrue, but the equating of a brain with a city is largely specious, especially when trying to understand cognition. Yes a brain regulates many low level parasympathetic processes. But it's the decision making and memory systems that we most want to understand and replicate in silico. And the coordination of a city of mostly independent self-interested humans is a poor analogue for the biological bases for a coherent thought process, unless it's only the medulla or pons that we hope to model.
- RocketSyntax 7y agoI am planning to solve this =) just cracked the inner workings of ANNs (future SHOW HN) and am going to read a book on computational neuroscience tomorrow.
- allovernow 7y agoNot to be discouraging, but if you're just starting to learn about artificial neural networks, you have a long way to go... That said, in the few years of experience I've had with ANNs, they really do seem like an intuitive analog for human learning, at a high level. And thinking about training problems in this way, approximately "what kind of training data would I need to train a small child to do this" can be more helpful than one might have otherwise expected. I think we're just a handful of major breakthroughs away from true AI, assuming compute and memory continue to scale. Certainly within 100 years.
- jedberg 7y agoThe author should go talk to some astrophysicists. They have a similar problem -- humans are unlikely to ever understand how the entirety of the cosmos works, but it's still interesting to learn about the small bits.
- stevebmark 7y agoI'm confused. This article doesn't say anything. It makes no points and has no insight. "There's a lot of data in neuroscience?" Is that the message? An unusual number of Nautilus articles frontpage HN like this one, where there doesn't seem to be any value in the article itself. What is going on?
- friendlybus 7y agoThe message is there are some potential new workflows. Revelation is running out of steam, processing huge quantities of data and relationships between nodes is the new hotness.
- Balgair 7y agoIt's that the author is undergoing a 'crisis', not that the field is. The title is clever, not descriptive. More broadly, there is a crisis. Our statistical methods/understandings are not working out in these large N-dimensional data sets, at least for the researchers that were raised on excel and not numpy. Aside: I'm surprised that the FAANGs haven't revolutionized statistics yet. When you have 'phase changes' where a LOT more data becomes available, you get to see very low probability events. It's happened in psych, in bio, in physics most famously, in politics, in economics, etc. We have a LOT more data now in for statistical use, but it's still just Poisson distributions and t-tests. What gives?
- johnc1231 7y agoIt might vary from field to field, but in genetics Excel is a complete nonstarter, and even numpy is not going to work on sufficiently large datasets. The project I work on, http://hail.is http://hail.is, was started to help deal with this.
- dang 7y agoIt doesn't look like that project has been discussed on HN before. One of you should definitely post it! If you email hn@ycombinator.com we can give you some tips on how to do that, and possibly also put the submission in the second-chance pool (described at https://news.ycombinator.com/item?id=11662380 https://news.ycombinator.com/item?id=11662380).
- m3kw9 7y agoI believe we are at the part where we think how the city works is by mapping it, but you got sewers, pipelines, everything underneath that you haven’t really dug into. There is a lot more inside a neuron that can be mapped. Let’s just say your map isn’t detailed enough
- m3kw9 7y ago“ connectomics and whether he thinks we’ll ever have a holistic understanding of the brain. His answer—“No” As a scientist to say something you don’t fully understand as impossible really pisses me off.
- chiefalchemist 7y ago"We don’t understand how their interactions contribute to behavior, perception, or memory. Technology has made it easy for us to gather behemoth datasets, but I’m not sure understanding the brain has kept pace with the size of the datasets." Exemplifies: - Data is not information. - Information is not knowledge. - Knowledge is not understanding. We've not even left the gate of the first tier. Both exciting and intimidating, but mostly humbling. Or should be.
- tus88 7y agoPhysicists don't understand gravity...neuroscientists don't understand the main...maybe the universe is a giant brain and the stars are neurons, the big bang was conception and we are bacterial growth. Better than all current theories.
- deleted 7y ago[deleted]
- mjfl 7y agoTo identify what the author feels is missing in neuroscience: in order to understand something, you need to figure out how to describe two things about it (1) what its state is at any point in time, and (2) how that state evolves in time. Connectomics gives you the beginnings to solve (1), but it doesn't go the whole way. There's a fundamental misunderstanding that you can collect exabytes of data and glean understanding from it just because how much you had to sweat getting the storage and collecting it. That's not how it works, it needs to have structure too. I wish more biologists / neuroscientists understood this.
- meowface 7y agoI think they understand this. This is demonstrated by there being little real understanding of ~300 neuron worm connectomes. The point of the article is discussing how mapping the full human connectome is only going to be a small next step towards understanding what's actually going on. That doesn't mean it's not worth doing.
- martythemaniak 7y agoI highly recommend this lecture by Jeff Lichtman, where he describes the machine they've built to slice the brain and the software they have written to visualize and make sense of this vast amount of data: https://www.youtube.com/watch?v=2QVy0n_rdBI https://www.youtube.com/watch?v=2QVy0n_rdBI
- elinchrome 7y agoMaybe it doesn't really matter.
- ImaTigger 7y agoI 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 7y agoAs 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-hemibrain.html 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.
- ggm 7y ago
- frozenport 7y agoThe real existential crisis is that nobody moving nueroscience forward has a nueroscience degree.