Why Does the Neocortex Have Columns? A Theory of Learning Structure of the World [pdf]

https://www.biorxiv.org/content/biorxiv/early/2017/09/28/162263.full.pdf

by blacksmythe • 9 years ago

237 73 9 years ago

biorxiv.org

skybrian• 9 years ago

There is also a summary at: https://blog.acolyer.org

mikhailfranco• 9 years ago

... probably not by pure coincidence.

p.s. specific dated link https://blog.acolyer.org/2017/10/25/why-does-the-neocortex-h...

brad0• 9 years ago

Great to see numenta in the news again. On Intelligence was the book that got me excited about biologically based machine learning. The methods that in that book is very different to anything else I’ve seen in current “trendy” ML.

electrograv• 9 years ago

Full paper here: https://www.biorxiv.org/content/biorxiv/early/2017/09/28/162...

I always enjoy reading analysis/ideas/intuitions about how the brain works, because it provides inspiration for machine learning improvements that can be applied in the real world.

That said, I’m still optimistically waiting for Numenta (and Geoff Hinton’s capsule theory) to set the new bar at one of the many difficult image/speech/language/etc recognition challenges.

Ideas are great, but at the end of the day science moves forward when we measure our ideas against reality. (To the credit of this paper, it does make a series of predictions, though those seem extremely difficult to measure in biological systems for the time being.)

adamnemecek4• 9 years ago

I'm probably talking out of my ass but I'm somewhat suspect of a cortex having straight up columns. I'm curious whether these are artifacts of the fact that linear algebra seems to be the dominant algebra in ML/modeling of human perception. Recently I've been dipping my feet into geometric algebra which seems to be the superior algebra for just about anything you can think of (human perception but also like all of physics, Maxwell's 4 equations are reduced to a single equation in GA) and it's particularly better for reasoning about spaces which this seems to be all about.

And unlike linear algebra it actually makes sense (e.g. why is cross product only in three dimensions?, wtf are determinants esp. in the context of matrix division all about?).

This blog post introduces GA and talks about it's relationship to human perception.

https://slehar.wordpress.com/2014/03/18/clifford-algebra-a-v...

gowld2• 9 years ago

Linear algebra is equivalent to geometric algebra so you wouldn't find columns less likely in GA.

Also, GA is usually hyped by non-mathematicians -- people who don't understand that it's just an alternative notation that doesn't add much to linear algebra.

Clifford Algebra, a.k.a. Geometric Algebra, is a most extraordinary synergistic confluence of a diverse range of specialized mathematical fields
It is the very simplicity and generality of Clifford Algebra that confirms its “truth”

kook alarms ringing

adamnemecek4• 9 years ago

Linear algebra is equivalent to geometric algebra

That depends on your definition of equivalent. They have the same computational power but things make sense in one and less in the other.

Also, GA is usually hyped by non-mathematician

What a loaded statement.

kook alarms ringing

You aren't the first one to say it. Nonetheless, the author is a researcher at Harvard it seems http://cns-alumni.bu.edu/~slehar/Lehar.html

Don't let the form fool you. You can also read any of the many books on the topic.

kkylin• 9 years ago

Mammalian cortex (well, in the species that have been studied closely) has both laminar and columnar structures. If you are interested in the biology side of things, take a look at

  http://hubel.med.harvard.edu/book/bcontex.htm

Caveat: it's pretty old, and is probably not quite right in a number of ways. But IMHO still a reasonable lay-person's introduction to what we know about the organization of the early visual pathway.

(Whether this has got anything to do with ML, I'm not in a position to say.)

lamename• 9 years ago

Cortical columns exist [1]; think perpendicular to the surface of the cortex [2, 2nd image, or just image search "cortical column"]. This is due to the morphology of cortical neurons. My understanding is that a column can be thought of as a functional unit, and passing information across columns adds complexity.

Of course biology is messy and there's tons of variation depending on which brain region you're looking at, but Visual Cortex was one of the earliest places this was observed. It gets complicated and detailed quickly, and I'm somewhat out of my element here.

[1] https://en.wikipedia.org/wiki/Cortical_column [2] http://www.mbfbioscience.com/blog/2012/01/neurolucida-helps-...

SubiculumCode2• 9 years ago

There are, of course lateral connections between columns, but the columns are very real.

I dont see what geomettic algebra has to with it, as the grandparent suggests.

rdlecler1• 9 years ago

I would have assumed it was reducing complexity filtering out noise and compressing information), not adding it?

wbl• 9 years ago

What definition of linear algebra doesn't include the Clifford functor?

madhadron2• 9 years ago

You're confusing Gibbs-Heaviside vectors with linear algebra. Linear algebra is the study of linear spaces and linear operators between them. Endowing those spaces with a norm or a dot product is usually as far as linear algebra courses go.

adamnemecek4• 9 years ago

Are you saying that cross product isn't a part of linear algebra?

rhyolight_2• 9 years ago

Here are some video resources to help explain this theory:

- https://www.youtube.com/watch?v=BvJJn9VS4rk - https://www.youtube.com/watch?v=-h-cz7yY-G8

pault2• 9 years ago

Here's another very interesting talk from Jeff Hawkins about modelling the neocortex: https://www.youtube.com/watch?v=izO2_mCvFaw

platz3• 9 years ago

Skeptical. Previously the brain was a machine consisting of drives and pulleys, just like the state of technology of the day. Now the brain is a computer that runs deep learning models. It would be one thing to just say the cortex has columns. It's another to go on and model what those columns are doing with linear algebra. The coincidence that this picture emerges at the same time as current fads in tech is too great to ignore

I am more interested in the work that is identifying different kinds of cells e.g place cells

platz3• 9 years ago

Alternatively if your real goal is just to develop some novel ML techniques, it would be better to not make claims about how the brain works and instead just try to draw inspiration using these thought experiments more as a 'muse'

freeflight2• 9 years ago

The coincidence that this picture emerges at the same time as current fads in tech is too great to ignore

It's no coincidence, I see it as a kind of reframing the issue from a different perspective/approach and for that, we use whatever seems the best framework of explanation we have at hand during that time. Gotta start somewhere, can't try to paint a picture without a frame and at least some colors.

One can easily see the evolution how the frameworks we use keep getting more complex, from drives and pulleys to computers and NNs, so there clearly is some progress in how we are trying to describe the workings of the brain/consciousness.

In the end, it's also about what's the actual goal here: Trying to understand the human brain/consciousness or trying to artificially create "consciousness" or rather something resembling it. The later doesn't necessarily require the former.

rhyolight_2• 9 years ago

Not consciousness, but intelligence. Big difference.

alphydan• 9 years ago

Do you have a reference for "when the brain was drives and pulleys"? I've never heard of such a theory.

Eddh• 9 years ago

I think he's referring to this https://en.wikipedia.org/wiki/Mechanism_(philosophy)

In particular, this Descartes quote "I should like you to consider that these functions (including passion, memory, and imagination) follow from the mere arrangement of the machine’s organs every bit as naturally as the movements of a clock or other automaton follow from the arrangement of its counter-weights and wheels."

heavenlyblue• 9 years ago

Unfortunately you're skeptical since you're mistaking philosophy for science.

Here: [https://arxiv.org/pdf/1706.06208.pdf], for example - they use deep learning models in order to classify the behaviour of in vivo neurons.

sigi45• 9 years ago

I do believe that our understanding on how our brain works is very close to what ML is doing.

There is probably a lot to learn about the underlying core structure but the basics will not change that much anymore.

varjag• 9 years ago

In the sense that there are neurons firing off each other sure. But it was known (and modelled in hardware) since early 1950s.

There is however a qualitative difference in the topology and function, or our very modest wetware wouldn't have been able to outperform about every deep learning model out there.

pault2• 9 years ago

I'm not an expert, but isn't the causality reversed? Neural Nets were developed based on research of the physical structures in the brain, right? If that's the case, it makes sense to make analogies between the two systems.

bbctol2• 9 years ago

Yes and no. Neural nets are somewhat inspired by neurons, but it's a pretty loose relation, and the more NNs improve, the less similar to the brain they look. Individual neurons are much more complex than the nodes of a neural network, and even if research into NNs provides insight into the way the brain works, it may be that the analogues of NN nodes are clusters or regions of neurons, or something more mathematically abstract.

thwd• 9 years ago

place cells

Suiting, given that your username means "place" in German :)

nategri• 9 years ago

Sax Russell, is that you?

A practically identical train of thought from that character shows up in one of KSR's Mars books. Alway found it an excellent point.

Tactic• 9 years ago

Also laid out quite well in the 1990 movie "Mindwalk". [1]

A movie still worth watching even though it is dated.

1. http://www.imdb.com/title/tt0100151/?ref_=nv_sr_3

evv• 9 years ago

The coincidence that this picture emerges at the same time as current fads in tech is too great to ignore

Neural networks weren't nearly as much of a fad back in 2004 when Hawkins published On Intelligence. They did a fantastic job of illustrating how the neocortex may be a hierarchy of these pattern recognizers (columns). It's a highly engaging read!

21• 9 years ago

Previously the world was made from Earth, Water, Fire and Air. Then quarks and electrons. Now they are talking about how the entire universe is a computer, and how each Plank area is just a qubit.

The coincidence that this picture emerges at the same time as current fads in tech is too great to ignore.

platz3• 9 years ago

I agree, it is suspicious, though let's distinguish between hard science and soft sciences, and between observation and theory

Cacti• 9 years ago

Jeff Hawkins gets shit on a lot in ML because his theories haven’t produced models with results as good as the mainstream approach, but I’m glad they keep working at it and keep coming up with interesting ideas.

Too much of ML these days is about some NN model that does 0.n% better than SOTA on some specific task. Then you change one tiny parameter and he entire thing breaks, and it turns out we didn’t understand why it was working at all.

daveguy• 9 years ago

I do think his reasoning and focus of what's wrong with ML today is good. Online learning, sequence learning and "motor" feedback are all critical.

Unfortunately he came up with this HTM and appears to be trying to cram all advances into the same framework rather than stepping back and thinking about how those critical features could be implemented differently.

Also as far as I know hebbian learning (like HTM) has achieved ~90% accuracy on MNIST while SOA is above 99% accuracy. So it's not a small gap between HTM and state of the art.

If HTM was near 98% it would be impressive (learning is faster and transfer better). I think alternatives or hybrids are necessary, but he seems stuck on his pet project.

That said, the principles and "what's important for intelligence" I think he is right on target and articulates those aspects well.

chplushsieh• 9 years ago

I just ran across a paper [1] from Numenta on Time Series Anomaly Detection using HTM last night which provide a benchmark[2] with some existing approachs. (But it seems to me there is no NN based approach in them. )

[1] Unsupervised real-time anomaly detection for streaming data http://www.sciencedirect.com/science/article/pii/S0925231217...

[2] https://github.com/numenta/NAB

davesque• 9 years ago

From a mostly outside point of view, I've wondered a lot about this. It seems a lot of ML/DL research that goes on nowadays amounts to educated guessing and tinkering i.e. "Based on our notion of what seemed right, we tried this other configuration of layers and/or activation functions and look; it worked".

I imagine at some point that the kind of research discussed in the linked post will begin to pan out more and more and will eventually lead to another period of rapid progress like that which followed Hinton's work on DL.

bluetwo• 9 years ago

Show me how HTM solves any multi-step process. Tic-tac-toe. Anything.

rhombocombus• 9 years ago

Cargo cult programming abounds in a lot of the ML stuff I read. It's complex and complicated, and I think it happens because a lot of the research happens in corporate environments with top-down pressure. I have worked in computational modeling of sensory motor systems, and it is really challenging work. This is way more interesting than banal model performance bragging!

simonster• 9 years ago

As a neuroscientist who just started doing ML research, I would call this paper cargo cult programming. If you cobble together a hodgepodge of ideas from neuroscience and build a network to accomplish some trivial task with no baseline to compare it to, I find it really difficult to take anything away from that. Ignoring the cortical column aspect, I'm not particularly convinced that Hawkins's model is a better approximation of biology than a typical deep neural network, just different (and likely far less capable, if you were to apply it to a challenging task). Why not start with a network that we know works and make a biologically-inspired change, and then see if that improves performance on a well-studied problem? If it does, then you have 1) an improvement on the previous network and 2) weak evidence that your idea of how the brain works may be right, if we assume that the brain is a highly optimized information processing device.

eli_gottlieb2• 9 years ago

Jeff Hawkins gets shit on a lot in ML because his theories haven’t produced models with results as good as the mainstream approach, but I’m glad they keep working at it and keep coming up with interesting ideas.

And also because Numenta's work isn't good, empirically-checkable neuroscience either.

TrinaryWorksToo3• 9 years ago

What part of Numenta's model is wrong? I certainly see some guesses and simplifications in their model, especially in poorly researched areas, but I figure that's the cost of trying to get things to work.

ewjordan• 9 years ago

Not sure whether that was a jab at Numenta or not, but I do think the combination of these two comments cuts to the heart of the PR problem for Numenta: they're neither trying to do 0.1% better on the classic perception benchmarks than other algorithms do, nor trying to publish accurate, testable descriptions of the true details of meatspace neuroscience.

To me, exploring alternative network architectures and algorithms seems an extremely worthwhile goal even if it's only loosely tethered to actual biology, but from a PR perspective they really need to be better about priming the conversation if they want people to care.

Bad (neuroscience-focused): "We're doing a lot of research on neuroscience, and finding some really interesting stuff, so we built a model that doesn't exactly match the way the brain works but is still interesting. No, we haven't tried to make it work to classify ImageNet test cases, that's not our goal. But look, it's closer to biology, and we have working code that we're playing with!"

Better (ML-focused): "We're developing a novel neural network architecture that performs online unsupervised learning using only local update rules. Though it performs competently at classic benchmarks X, Y, and Z when a small WTA layer is thrown on top, it can also tackle problems A, B, and C that classical deep learning networks can't make any progress on."

To be fair, I'm not even sure if Numenta's networks could perform competently at any classic benchmarks (I'm guessing that if they could, it would take some work to get them to do so), and I have no idea what new problems it could work on. But they really do need to reframe the conversation and emphasize that sort of innovation if they want to be taken more seriously - focusing on neuroscience underpinnings is not a great move if they're not engaged in research that can actually win over neuroscientists, and just pointing out that they're focusing on those things is not a way to win over industry ML folks if they don't have any results to point at.

tziki• 9 years ago

I thought he gets shit because he patented his approach instead of making it available for everyone (please do correct me if I'm wrong).

TrinaryWorksToo3• 9 years ago

It's GPLv3: https://github.com/numenta

nopacience• 9 years ago

Understand how the brain works means understand how we the humans see and understand the world and ourselfs within it. The brain organically and selectivelly selects what to learn and which information shou ld be retained. Our brain receives training since birthdays. Then the family implements some of their own training then school and world. The brain never stops.

Great job Numenta !!

Upvoter33• 9 years ago

The world needs more work being done in the way Hawkins and co. are doing it, and less in the mold of most deep learning/ML work. Why? He's actually trying to connect building intelligent machines with biology. This is a huge problem, but so few are working on it. Rather, we are all distracted by deep learning because of its recent successes in very specific problem areas. In a few years, when we run into its limits, Hawkins and people doing work like this will have a chance to shine (if they produce something that works, of course).

pizza• 9 years ago

But wait a minute... the world is already doing quite a good deal of work on this type of problem. Maybe not enough, but it's been goin on

https://en.wikipedia.org/wiki/Computational_neuroscience

bbctol2• 9 years ago

Deepmind does a ton of work in meat neuroscience, and are actually publishing useful research in the field (a recent if somewhat controversial review at http://www.cell.com/neuron/fulltext/S0896-6273(17)30509-3) And they're about the most prominent deep learning group I can think of.

dontreact• 9 years ago

Computer vision and NLP are pretty broad problem areas with many applications. These technologies are already powerful enough to influence the world in many positive ways. I wouldn’t say that Actually realizing this potential is a “distraction”

Double_Cast• 9 years ago

Error! Problem, or Page Not Found
Sorry, the page you were looking for does not exist.

Link is broken. From browsing, I believe the correct link is

https://numenta.com/papers-videos-and-more/resources/layers-...

Avery3R• 9 years ago

The link works fine if you disable javascript. Still weird though

mholt• 9 years ago

I'm getting "Error! Problem, or Page Not Found Sorry, the page you were looking for does not exist."

Edit: Weird, I just closed my browser and tried again, and the article looks like it flashed into the screen then was replaced by the error message. Happens repeatedly on Chrome on Android...

dang• 9 years ago

I'm seeing that too. Ok, we'll change the URL from https://numenta.com/papers/why-does-the-neocortex-have-layer... to the pdf of the paper. There's also a summary at https://blog.acolyer.org/2017/10/25/why-does-the-neocortex-h... that other commenters have mentioned.

Why Does the Neocortex Have Columns? A Theory of Learning Structure of the World [pdf]

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