Sand in the Brain: A Fundamental Theory To Model the Mind

An anonymous reader writes "In 1999, the Danish physicist Per Bak proclaimed to a group of neuroscientists that it had taken him only 10 minutes to determine where the field had gone wrong. Perhaps the brain was less complicated than they thought, he said. Perhaps, he said, the brain worked on the same fundamental principles as a simple sand pile, in which avalanches of various sizes help keep the entire system stable overall — a process he dubbed 'self-organized criticality.'"

Comments

As a physicist:

[drolli]

Dear fellow scientists, admire us for the 1% of the cases when things like "oh i have a very simple theory about this" are brilliant and dont hate us for the 99% of the cases where this is just idiotic and arrogant.

Re:As a physicist:

[Bite The Pillow]

"I have a simple theory" is the result of multidisciplinary collaboration, in which new connections get made by someone who understands the patterns and foundations of apparently, but not really, unrelated subjects.

"Your field is fundamentally wrong" could be idiotic and arrogant. Or it could be something so intrinsically obvious as two plus two does not equal four, or God exists (or doesn't). Democrats are evil, Republicans are evil, and the focus of neuroscience should be about how the system maintains criticality.

I hardly consider this arrogant. Arrogance is an inflated sense of superiority, and usually the arrogant person knows, on some level, that it is just a front. Just stating something gives them a feeling of superiority, triggering pleasure centers. Being proven right, in public, is quite possibly the best thing ever because it presents a factual basis for what is, at least occasionally, a fantasy.

People who know, or believe, something truly and completely, do not do this. Believers seem to rebel against any contrary information, actively rejecting it. Knowers present clarity of fact. They may be completely wrong, and may cross over into being believers, or they may disbelieve when proven wrong.

Because science is fundamentally about trying to prove others wrong (and either failing or succeeding), it is important to distinguish among a deep-rooted belief, transference of knowledge (even if it is mistaken), arrogance, idiocy, and the scientific method.

As far as physicists specifically, I would expect that biological and chemical functions would have some level of physics at their core. Whether it is a true correlation or just similar in appearance will have to be decided. But I would prefer to have an asshole physicist say everything is wrong and be right 1% of the time, and the rest just be brushed off like the guy from marketing at the Christmas party.

Nitpick the oversimplified psychobabble if you like, but the point is that words mean things. And attributing intent to people based on their ideas, and even their words especially if they are not a native speaker, is a great way to completely miss the point. Not debating that it's an issue - but it is far too easy to dismiss an interloper from another discipline as arrogant - all the easier if you believe in your field of study, as opposed to knowing it.

oblig xkcd

[bistromath007]

http://xkcd.com/793/

The really interesting thing will be when Randall does a comic about how you can get easy upvotes for "oblig xkcd" posts.

Re:oblig xkcd

[the eric conspiracy]

This comes from the fact physicists are used to working with differential equations that they can't prove existence or uniqueness of a solution for.

So they simplify, i.e. 'assume a spherical cow' as a way of living.

Of course that almost never works for a real system so they go off and try to understand the universe one particle at a time.

Re:oblig xkcd

[phantomfive]

That is what I thought of too, but in this case neuroscientists agree with him. If you read the article:

But over time, in fits and starts, Bak’s radical argument has grown into a legitimate scientific discipline. Now, about 150 scientists worldwide investigate so-called “critical” phenomena in the brain, the topic of at least three focused workshops in 2013 alone.

Just goes to show that xkcd is not the answer to everything.

Re:oblig xkcd

[JanneM]

I think this SMBC comic is very appropriate as well: http://www.smbc-comics.com/?id...

Sand in our Brain

[ComputersKai]

Hey, at least now we have an excuse for stupidity!

alright just kidding, but seriously...if our brains really are just jumbled masses of impulses...

Then jumbled masses of impulses must be pretty darn good.

Re:Sand in our Brain

[phantomfive]

Did you read the article? If you understand it, please explain it, because his theory makes no sense to me whatsoever.

I don't understand how it helps us understand intelligence. I mean, I'm sure things happen in the brain in waves, you could say the same thing about a computer as well, when I start JAVA there is a mass of RAM allocations, that suddenly get released in varying sizes when the GC happens. But I'm not sure that's a helpful way of looking at things......

Re:Sand in our Brain

[Charliemopps]

Ok, well... my understanding of it is that nature is made up by random events. If those events were all there were, you'd get white noise. A perfectly even randomness. However, nature also has laws. With regard to sand, there's gravity, and slope, friction, etc... and that means these randomly falling grains of sand, on the macro scale, end up forming patterns. These patterns end up being very complex but predictable with statistics. Understanding a dune from the point of view of a grain of sand is nearly impossible. You just need to know the rules the system is following and then you can make accurate macro-scale predictions without having to compute every grain of sand in the dune.

The arguments made its way into nearly every branch of science now. Our attempts at brute forcing nature, and trying to connect the sub-atomic scale with the macro scale have mostly failed. But it now seems that maybe nature doesn't work that way. Nature seems more to work based on sets of probabilities, and particles seem to work more like "attributes" than matter. So perhaps the brain works like this to. It's a collection of chaos, bound by rules. Those rules cause the microscopic chaos to form patterns on the macro scale.

Re:Sand in our Brain

[Anonymous Coward]

The theory is an overarching idea of how the brain works and best makes sense when compared to other theories. One (not this theory) way to think of the brain is that it is like a computer, with specialized areas which each calculate for specific functions and having a whole mess of complicated parts that evaluate against each other and somehow all work together. This theory instead sees the areas not as having logically complicated interlocking parts, but as each part having a sort of pile where if enough stuff (hormones, electric potential, etc.) is piled onto it, it performs its action. Often this action will include piling more stuff onto other areas piles and then resetting to a baseline.

  This theory better explains how the brain can operate in a logical, deterministic fashion while allowing for easy error correction. A computer-like brain would continue to use bad data and damaged instructions could cause whole parts of the brain to fail permanently. The "piles of sand" resetting to a baseline model would accept the bad data once, reset to base, and move on. Damaged instructions (mislinked neurons, brain damage, etc.) could continue to send to much or too little "stuff" to other nodes or wrong nodes, but a system which monitors what is considered "normal" and resets to such will eventually be able to re-normalize every node that isn't directly damaged.

Re:Sand in our Brain

[barlevg]

The pendulum regarding self-organized criticality is beginning to swing back in the other direction: many researchers now believe it's being over-applied, and the "power law" distributions that people see for natural phenomenon that are "evidence" of S.O.C. have been shown to not actually obey power laws (it's really easy to make these kinds of mistakes when you make your graphs on log-log scales). Sorry if that was a bit dense, but the long and short of it is that not everything that is being touted as an example of self-organized criticality likely is. For instance, the Bak–Tang–Wiesenfeld sandpile (Bak being the one from TFA)? Turns out it's a HORRIBLE model for how real sandpiles behave.

A lot of the above really needs citations, but I'm too tired and lazy, sorry. To "back this up," let me just say that I have a Ph.D in physics, specializing in nonlinear dynamics, and the above comes from a graduate-level course I took from a professor who knows her shit.

Re:Sand in our Brain

[wanax]

The linked article was horribly written. I'll give a shot at trying to explain it (or rather, a really, really simplified version).

Two of the fundamental problems that neural circuits must solve are the noise-saturation dilemma and the stability-plasticity dilemma. The first is best explained in the context of vision. Our visual system is capable of detecting contrast (ie. edges) over a massive range of brightness, spanning a space of about 10^10. Given that neurons have limited firing rates (typically between 0 and 200hz), there needs to be some normalization criteria that allows useful contrast processing over massive variations in absolute input (more on this later). The stability-plasticity dilemma is that the brain needs to be sufficiently flexible to learn based on a single event (let's say, touching a hot stove is a bad idea), but once learned memories have to be sufficiently stable to last the rest of a creatures' life span.

The stability-plasticity dilemma implies that neural circuits must operate in at least two (as I said, very simplified) distinct states, a "resting" or "maintenance" state, and a "learning" state, and that there is a phase-transition point in between them. Furthermore, these states need to have the following properties regarding stability:
1) the learning state must collapse into the maintenance state in the absence of input (otherwise you get epilepsy).
2) reasonable stimulation (input) during the resting state must be able to trigger a phase change into the learning state (or you become catatonic).

Many circuits/mechanisms have been proposed to explain how the brain solves these dilemmas. Most of them involve the definition of a recurrent neural network using some combination of gated-diffusion and oscillatory dynamics to fit well known oscillatory and wave-based dynamics that have been recorded in neural circuits. Some of these models employ intrinsic learning using a learning-rule (ie. self-organized maps) while others are fit by the researcher. One key point about this class of models (as opposed to the TFA approach) is that they have a macro-circuit architecture specified by the modeler. Typically these models are at least somewhat sensitive to parametric perturbation.

TFA describes another approach, which comes out of research on cellular automata done by Ulam, von Neumann, Conway and Wolfram. This approach posits that parametric stability and macro-circuit organization is only loosely important so long as the system obeys a certain set of rules regarding local interaction (could also be through of as micro-circuit) because it will self-organize to a point of 'critical stability'. In the the two-state model described above, this approach predicts that neural circuits are always at a state of 'critical stability' where maintenance occurs through frequent small perturbations or avalanches, and any new input will trigger a large avalanche, causing learning. Bak has proposed this as a general model of neural circuit organization. One trademark of these type of models is that they show 'scale free' or 'power law' behavior, where the size of an event is inversely proportional to its frequency by some exponential function. Some recent data has shown power-law dynamics in neural populations (a lot of other data doesn't show power-law dynamics).

One big problem with the critical stability hypothesis is that it doesn't deal well with the noise-saturation dilemma: it needs to cause the same general size of avalanche whether it's hit by one grain of sand, or 10^10 grains of sand.

None of this is particularly new, neural-avalanches (albeit in a different context) were postulated in the early 70s. Could some systems in the brain exploit self-organized criticality? Sure, but there is a lot of data out there that's inconsistent with it being the primary method of neural organization.

Re:Sand in our Brain

[wanax]

With regard to question 2) No.
Question 1 is an ongoing field of research. Some of the work that I've found helpful in approaching the question:
-The Computational Beauty of Nature (Gary William Flake)
-Barriers and Bounds to Rationality (Peter Albin; there are free pdf copies available online).
-A New Kind of Science (Stephen Wolfram; also available free online).

Re:Sand in our Brain

[wanax]

Actually, since neurons have functional homeostatic pruning and nonlinear membrane responses, there are quite large values of zero when we're recording firing rate.

Re:Sand in our Brain

[mikael]

Look up "boids". Each critter has a field of view and a current direction. It only responds to what it sees in that field-of-view. If other critters start running, it starts running too. If they stop, it stops. With fish, the minute one turns, there is a flash of light. That instructs all the others to turn as well, providing the flash is bright enough. Maybe it takes two or more.

Re:Sand in our Brain

[master_p]

Could it be that neurons simply don't store new information except the first time and that all experiences are stored as an incremental backup, i.e. it's only the changes that are stored?

This solves the stability-plasticity dillemma: the first experience that comes is stored as a whole, and then similar experiences are only stored as a delta from the initial experience - thus allowing the brain to maintain some 'forever' experiences like touching a hot stove but also be flexible enough to remember new experiences.

This can also account for the deja vu effect - recalling experiences that are similar.

As an observer

[fyngyrz]

The objective reality is that this process has been observed to happen in the brain. Repeatedly; consensually; experientially.

The open question, at least for me, is, is there any reason to think that this is the only, or even the primary, mode of neural operation?

Sand will indeed avalanche following the power law when it's poured on top of itself. But it does something completely different when it is suspended in turbulent water, or melted into glass, or just sitting there on the beach (seems to have an affinity for the inside of bathing suits as I recall, though it's been a while.)

Perhaps avalanche at criticality is "the" answer. But I think we're quite some distance from declaring that particular win. I'm all for the exploration, though.

Re:As an observer

[Anonymous Coward]

I think it should be noted that the human brain also has an affinity for the inside of bathing suits. . .

Re:As an observer

[Artifakt]

Except we are seeing many cases where it is counterintuitive even to working scientists in their own fields, just which explanation is simpler.

            For example, Guth's inflationary hypothesis in Cosmology has resulted in a prediction that certain constants must be random (because otherwise, there's the implication of something we might as well call God behind the non-random values). A hypothesis that invokes God is probably not the most simple - anything that might merit the name of God is likely to be more complex than the very universe it 'explains'. Fair enough, but random values seem to imply an infinity of parallel universes, which however will never be detected by real science, only in science fiction. An infinity of untestable phenomina as the outcome of a model hardly makes that the preferred model by Occam either. Last I looked, neither one of these interpretations of the inflationary hypothesis* has been mathematically shown to be the more simple of the two. If people who have had some real impact on the specific field (i.e. Hawking), can't really agree on what they mean by simple, Occam's Razor isn't working very well.

          This has shown up in several other areas of science, for example recent math proofs by computer that are so complex there's a real chance the computer made errors during the months it was crunching numbers for the millions of steps required. Once a proof is too complex for humans to even check, how can we possibly tell whether it is more complex than another proof or not? (Counting lines of code is not a very good measure there). And while I'm hardly up on all the issues in the "universe as a giant computer" debate, I've seen arguments from some of the pros in that field that seem to show there's problem with determining which explanations are the most simple there too, and I've heard at least one working scientist in the field of sexual selection pressure complain about the same thing.

* The recent Antarctic discovery might argualbly elevate Cosmic Inflation from hypothisis to full fledged theory if it wasn't there yet. For those who think it was a theory already, these observations would seem to place it on even more solid ground, in much the same way as Crick and Watson's work helped strengthen the claim of Evolution to be a well tested and heavily supported theory. But, not being able to predict whether the initial universal constants were random or non-random is a real problem when it comes to proclaiming Cosmic Inflation has the status of a solidly tested theory, no matter how much other evidence scientists gather.

Re:As an observer

[mikael]

There was an idea in computing several decades ago about "asynchronous computing". The idea was that you could get rid of the need to have all the different regions of your silicon chip clocked at exactly the same speed. Instead, data would move between different units at different speeds according to demand. If a particular circuit wasn't used, you could put it in a low power state, if something was being filled up with data, you boosted the clock speed. You end up with data "flowing" through the system or data-flow- computing.

So it's much similar to the brain where different regions light up under fMRI analysis as oxygen flow increases as they are used. And scientists have a good idea what different regions of the brain do - usually a high-level function like generate-muscle-motion-to-say-phrase or recognise-name-of-object-from-picture. From other methods of MRI scans, they have identified the pathways where different parts of the brain communicate along, and are able to visualize these as "connectograms", Phineas Gage is the best example.

Re:As an observer

[Viol8]

Except for standard computers it doesn't work so well since the CPU is almost always faster than everything else so the other parts of the system almost never have a chance to kick back and relax - its usually the CPU waiting for them to do something. Also a serial computer is pretty much like a road - everything moves at the speed of the slowest vehicle/component. Slow one thing down and you slow everything down. Parallel systems improves things a bit - you now have a multi lane highway - but even so , the same basic rules still apply to each lane.

This is more like what's going on.

[Anonymous Coward]

http://www.smbc-comics.com/?id=2556

Ick

[BrianPRabbit]

Can We stop conflating the brain with the Mind, please? The Mind is a philosophical concept and the brain is a physical organ. The two ideas are distinctly different and their conflation speaks of gross ignorance.

Re:Ick

[ceoyoyo]

Can We stop artificially dividing the brain and the Mind, please? The two ideas are likely the same and their distinction speaks of lingering medieval mysticism.

See that? I even put in the gratuitous capitalization!

Re:How come smart people usually die young ?

[arjun.jrao]

Knuth is still alive. Planck, Shannon, Newton and Feynman are examples of exemplars who lived full long lives.. these are just the ones that come to mind. I'm sure there are many more.