Artificial Neural Nets Grow Brainlike Navigation Cells | Hacker News Reader

Artificial Neural Nets Grow Brainlike Navigation Cells

quantamagazine.org

151 pointsdigital5547 comments

To draw too many parallels here would be like comparing stick figures to still life paintings and proclaiming "They're both flowers!" While it might be true you won't learn much about still life paintings from stick figures.

At best this research says something about the task of navigation and optimal representations for that task rather than anything profound about neural networks other than they can both optimize for some task—which should surprise no one.

Things get over-stated because there would be nothing worthy of publishing otherwise. It's the same (f'd up) model that drives the MSM, etc.

The Internet...access to all the information in the world; most of which isn't new or worth knowing about. Bring a shovel. You're gonna need it.

Just because it's not some profound connection doesn't mean the analogy isn't interesting in itself. The author maybe should have toned it down but it was still interesting and blog post worthy IMO.

Perhaps. But why overblow it? That makes me suspect.

I confess, I've spent far too many cycles reading "interesting" things only to have close to no memory of them 2 or 3 days later.

My conclusion? Novelty does not equal an increase in the quality of my life.

That's not to discount (less impactful) entertainment; only to say (that for me) interesting isn't enough any moew, it's too often not worth the time suck.

There are literally dozens of us who feel the same way, yet I for one am still looking for a way to cope.

Back in the old days, people paid for this. I don’t mean to sound snarky, but in a small way, coping with the novelty is my job (as an industry analyst) and some people chose to pay for that. If you need to know, but can’t cope, then you pay someone else to do it who can specialize.

This does not solve the problem of things that get published which you don’t need to know and which flood your everyday life. I can’t help but feel that that is an intrinsic part of the internet- Both a feature and a bug.

If you're still on FB, I use groups __a lot__. If there's a topic I enjoy, I put a bunch of my friens into it, and then hope for quality sharea.

Mainly though, I'm just trying to be more mindful. I have to ask myself "is this __really__ worth my time?"

Finally, more (print) books and print mags.

I think the formula has to be interesting + relevant x challenging = memorable. Trivial novelty is empty, difficult novelty is empty, but difficult, relevant novelty is enriching. If I had to work for knowledge which relates to a broader schema, I find it never goes away.

That's how the brain operates: No pain. No gain.

I sometimes wonder if the UX mantra "don't make me think" is best. It could be said another way as "make it unmemorable."

Sure there are times that's good. But there are plenty where it's not. We all fall for "oh. I'll remember that." But we don't.

Whwn we work to attain something the brain is wired to value it more.

Yea same thing happened with fractal patterns and nebula's in deep space 20 years ago. (Also neurons too)

Any source or keywords to look for this?

http://fractalfoundation.org/OFC/OFC-1-6.html

> The “grid units” that spontaneously emerged in the network were remarkably similar to what’s seen in animals’ brains, right down to the hexagonal grid.

Could someone with more experience in ML explain what this means? In what sense do NN cells have positions or geometry? What are the NN heat maps below the quote showing?

The neurons themselves don’t form grids. A map of the points where firing of a grid neuron in happens the real world forms a triangular/ hexagonal grid.

These neurons seem to have discovered what board gamers found out much later - hexagonal grids are better for calculating movement.

https://en.m.wikipedia.org/wiki/Grid_cell

Ah, thank you. That makes a lot more sense.

Link to pdf: https://sci-hub.tw/https://www.nature.com/articles/s41586-01...

Thanks. I was curious what it takes to get it from Nature. For the low low price of $199 you too can buy a subscription!

As a roboticist just beginning to read ML papers (to help in this very field!), this information would otherwise just be out of reach.

On a train and don't have great wifi -- but there was a super-cool poster at ICLR which demonstrated that training an RNN to perform dead-reckoning naturally produced grid-like cells. Is this an extension of that work? Or an independent discovery of the same phenomenon?

I'm trying to reproduce that original work, so far without success.

I think you're referring to this paper:

"Emergence of grid-like representations by training recurrent neural networks to perform spatial localization". https://arxiv.org/abs/1803.07770

It appears to be from Columbia vs DeepMind with different authors.

Are you referring to the Cueva paper? I also spent a considerable amount of time trying to replicate it without success. Do you know if the author has released any code yet?

I wonder what the coordinate plane is for the ML visualizations and how it relates to same for visualizations from a physical brain. Seems ripe for gaming.

As it is dead reckoning, I assume it is relational/velocity.

This supports the idea that, increasingly, machine learning is coming back full circle to support neuroscience. Previously, AI researchers looked at the brain for inspiration and now more than ever neuroscientists are being inspired by advances in deep learning, etc.

I don't doubt that there are many similarities between how machine learning works and how brains work, but this seems like a pretty myopic trend of confirmation bias. Neurons and brains are so much more complex than machine learning and it will be really unfortunate if we limit ourselves to the machine learning model in neuroscience.

maybe the functional nodes (wetware neurons vs software neurons) are very different, but it seems like the way they are manipulated via back-propagation, pooling, recurrence, layerings, etc, are similar, right?

because, at the end of the day, it's more about how behavior is emergent than how behavior functions physically, I would say.

I would guess that if we ever get to some true sci-fi AI "consciousness", it would just be a hyper-scaled version of what we already have. But that's just fun speculation.

This is not correct, artificial NNs are not functionally related to neurons in the brain. Nothing like back-propagation has been observed in real neurons. The early layers of a CNN may be similar to early perceptrons in the brain, but beyond this any connection between the two is fantasy.

Nope. Brains don't use layers or back propagation. CS NN's are really just a cool name for math that has almost nothing in common with how the brain functions.

There is a lot of misinformation about how the brain works. For example you see a lot of drawings with different parts of the brain doing different things. However, if you look at an actual brain almost none of this is physically obvious. At best those diagrams show what stops working when that part is damaged, though again plenty of people have very different structures and it still mostly works.

Honestly, this is more like having invented solar cells and then saying that that's how trees work. Turning light into energy is just a small part of what trees do, and solar cells are only an approximation of the idea but the actual implementation is very very different from photosynthesis.

Real cells use Hebbian learning, which in some cases is equivalent to back-propagation, but is way less efficient. Otherwise, yes, many of the techniques used in ML is also used in the body. Not only ML, actually, but also electrical engineering and surely many other fields. The more you learn about the body the more machine-like it will look to you. In some respects. In others, it's fucking space technology. Nanobots exist, they are called proteins, and each of your cells have hordes of them, for example.

The big difference is that biological neurons emit binary outputs. Because of this, there’s no gradient and they can’t be trained by SGD.

There's phase and frequency information in neural spike trains.

But biological neurons are also stochastic, and the firing probabilities are continuous, so you can do SGD on them.

Well, I think it may not just be Hebbian learning but likely Balanced Amplification learning [1] as well which propagates quite a bit faster. Promising paper came out recently echoing this phenomena globally with a model of visual perception in the macaque brain[2].

[1] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2667957/ [2] https://www.cell.com/neuron/fulltext/S0896-6273(18)30152-1?

Yes and no. On the one hand, yes, theoretical neuroscience does use some ideas originating in machine learning and computational statistics. So does computational cognitive science.

On the other hand, in these fields we set ourselves a different explanatory task: point to the real brain, the real body, and real behavior, and explain them in their full complexity. When asked, "how does this explain the real world", machine learning feels no more compunction than an aeronautical engineer asked to explain wings.

This would be great, if only machine learning actually built its engineering artifacts from solid normative principles! Instead, it mostly just hacks things together, as a result of which, its lack of compunction comes across as a reluctance to stand up to the challenge of explaining any aspect of the real world at all, which becomes embarrassing which the machine learning world begins opining on the nature of intelligence and such. Meanwhile, if you ask cognitive scientists and neuroscientists to opine on the nature of intelligence, we have many fewer cool demos, but many more principles and better scientific evidence.

Hacks as a human-driven search driven by competitive forces to find structures that solve human-like tasks. Though it's not guaranteed to produce biological neurology (unless it is somehow a singular solution), it is at least convergent evolution.

this would be the dream, but biological neural networks don't do gradient decent, so have little to do with ANNs until we discover how they really train.

It is perfectly possible to build neural networks with other functions besides gradient descent.

I agree. I'm saying we don't know how biological neural networks are trained, which is a pretty important part of any learning construct, so we can't really know how actually similar artificial neural nets are.

What other methods are there for training a neural network? Even blind evolutionary algorithms (assuming each generation is initialized to be somewhat 'near' or 'similar' to its progenitors) are effectively descending a gradient.

To my knowledge they explore solution space but not through a gradient descent but by probing.

How can you perform a GD on non continuous function? Genetic algorithms and evolutionary methods can explore disjoint domains and functions hard from analytical pov.

Just because they can be used for gradient descend doesn't mean they use it.

It would be remarkable if biological neural networks don't use gradient descent. They very likely do even if it is a highly approximate gradient. Neurons are optimizing for some objective, and the gradient is usually a very efficient way to do that optimization. It would be remarkable if evolution missed out on such a thing. Even simple Hebbian learning can be viewed as a gradient descent technique.

Gradients can be calculated in remarkably simple ways. Look at Hinton's recirculation networks for example.

I think you meant to say backprop.

How would brain do gradient descent without backpropagation?

Microcircuits could descend the gradient of a function they can evaluate locally.

why use "deep reinforcement learning" at all, is there any basis to believe it's a valid model of biological learning?

Natural reinforcement learning [1] was known before the mathematical one. Of course, people often mistake Markov chains with reality, but they still can be useful even if in completely unexpected ways like with DRL.

[1] Rescorla RA, Wagner AR. A theory of Pavlovian conditioning: variations in the effectiveness of reinforcement and nonreinforcement. In: Black AH, Prokasy WF, editors. Classical conditioning II. New York: Appleton-Century Crofts; 1972. pp. 64–99.

looks like reinforcement learning people have been missing out on the last 70 years of cognitive psychology. Stimulus-response theories of classical conditioning have been a subject of controversy since at least the 50s[1], these are very poor cognitive models

[1] https://en.wikipedia.org/wiki/Behaviorism#Criticisms_and_lim...

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