Slashdot Mirror
Adult Brains More Flexible Than Previously Thought
stemceller passed us a link to the official site for Johns Hopkins, which is reporting on some research into cognition. Generally, doctors have understood our best learning to be done at a young age, when the brain has a 'robust flexibility'. As we get older, our brain cells become 'hard-wired' along certain paths and don't move much - if at all. Or, at least, that was the understanding. Research headed by the hospital's Dr. Linden has taken advantage of 'two-photon microscopy', a new technique, to get a new picture inside a mouse's head. "They examined neurons that extend fibers (called axons) to send signals to a brain region called the cerebellum, which helps coordinate movements and sensory information. Like a growing tree, these axons have a primary trunk that runs upward and several smaller branches that sprout out to the sides. But while the main trunk was firmly connected to other target neurons in the cerebellum, stationary as adult axons are generally thought to be, 'the side branches swayed like kite tails in the wind,' says Linden. Over the course of a few hours, individual side branches would elongate, retract and morph in a highly dynamic fashion. These side branches also failed to make conventional connections, or synapses, with adjacent neurons. Furthermore, when a drug was given that produced strong electrical currents in the axons, the motion of the side branches stalled.'"
Where are the comments!!
No, the science is settled. Adult nerve cells don't wriggle around, everyone knows that. There's no need to look. Nothing to see here, move along.
They just normally prefer not to do so.
I had to fight them for a long time to use it, but now even my parents (in their 60s) suffer from internet withdrawal if they go without for a few days.
There's a branch of neural net studies that focuses on a technique called entropic topography. Essentially, it involves random evolution of just the fringes of a digital neural net. That is, much as this John-Hopkins study has found, a rigid core is kept. It is only the neural subnets branching off that undergo synthesis and morphing.
While there are various deterministic algorithms that are used to evolve neural nets, it's only recently that we've begun seeing randomness used. This has an added benefit of bringing in unexpected mutations, which really don't happen with the deterministic algorithms.
Some advances from the study of Lei topographies have also lead to breakthroughs recently, where some of the more complex, yet deterministic, algorithms have had entropic terms introduced in order to bring in an element of randomness. These neural nets are probably the closest to the human brain, as they introduce the random mutation that is so prevalent within the human species, while also following the constraints of this new-found core neural path.
This whole thing is about mice brains actually, how do we know how that applies to adult human brains? The RTFA doesn't seem to say much about that..
You just got troll'd!
how Apple computer started using Intel chips. Neuronal flexibility!!
Here's another article on the same topic.
This is kind of what I'd expect, actually. Even if an adult mind was completely plastic, as people learn of the type of experiences that will come to them, they're going to quickly learn to categorize them, and which kinds of categories tend to work with more and more experiences.
It's like as a programmer learns of which coding constructs work for which situations... they learn it becomes more important to worry about understandability rather than speed, and to code with clear structures they can pick up later if and when they need to clean up misunderstandings later. The default practice becomes a sort of robust defensive form, that requires the fewest changes over the widest plausible set of needs - while still doing the job of completely enumerating the problem set needed.
I'd expect that even with minds unhindered by age, the same sort of defensive practices programmers pick up would have analogues in most other realms of experience that mankind goes through. That would then, be easily confused with a mind unable to rapidly change, because such wide change is then rarely observed.
That said - there are more concrete bits of evidence that complicate things - such as rates of new language adoption between adults and children... but again, there's also evidence that some adults can still pick up new languages rapidly. Perhaps those same defensive practices act as a 'language censor' to 'wasting time with confusing sentence structure' - or perhaps there really is some factor of truth to the hardware limitations of an aging brain. Hard to know for sure until we get the computational nuerobiology tools in place to be able to strictly test such things... I'm really happy to see the progress so far though.
Ryan Fenton
i'm getting older, everyone does, but media and "science" try to put in your mind that you are not capable of learning new things or think like a young man, is a trap, the truth is, you can *always* learn... pick a book, a class, and try yourself.
My brain can stretch like silly putty.
I hate this view that some how results of tests on animals don't apply to humans at all. It's simply not true, almost every major medical advance has been tested or researched on animals like mice first. the simple fact is mammals bodies all work in very similar ways.
Having worked in a lab (disclaimer: not as a scientist) I learned that there are loads and loads of promising treatments for cancer and such that work great in mice, and never translate beyond. Even a casual glance at immunology from a layman's perspective reveals your statement to be utter bullshit; there are many, many diseases and afflictions that are species specific, sometimes highly so.
Anyway...it is entirely plausible that this ability to re-purpose brain cells is a plus for mice in survival/adaptation, where they have very little brain capacity at their disposal. We have loads at our disposal, and tend to build a lot of generally useful knowledge..ie, we build tools, literally or figuratively, and apply those 'real' tools or knowledge/skill 'tools'. Mice do not do either. We're more "general purpose", so maybe we don't *need* the ability to re-learn, since our learned skills are so broadly applicable in a survival sense.
Please help metamoderate.
Or maybe young people are smart enough not to clog up their brains with information that can be more easily and accurately recorded elsewhere. If all our fancy devices somehow stopped working, there would definitely be a period of confusion, but people would adapt. They'd go back to using their memories (or pen and paper.)
Technology isn't conflicting with our brain's evolution; it's extending and enhancing it. One less phone number to remember is who knows how many neurons that don't have to waste time storing and retrieving it. You might question whether young people are using this freed memory space to good use (for the love of all that's holy, I do NOT care about who won the latest reality show or what celebrities do in their spare time), but I think that it's a mistake to view this phenomenon as a fault.
Let me get this straight. An adult may be just as capable of learning something new as someone younger. But they aren't as capable of re-considering things they already know. I.E. they have a harder time changing their already established brain structure but forming new structures isn't a problem. Anyone?
Why is this article tagged with 'ronpaul' and 'ronpaulisanazi'? I thought this was slashdot, not digg. Why don't we just tag the article with 'omgiphonejailbreak' and '10waystoimproveyourwebsite' while we're at it?
Even a casual glance at immunology from a layman's perspective reveals your statement to be utter bullshit; there are many, many diseases and afflictions that are species specific, sometimes highly so.
He stated that it's "not true" that animal tests don't apply to humans at all (true), that almost every major medical advance has been tested or researched on animals like mice first (true, at least since the mid-twentieth century), and that mammal bodies work in very similar ways (true).
What you said is also true--that despite the huge similarities there are also significant differences--but that doesn't make his statement "bullshit"... perhaps merely "incomplete."
I support your point in general, especially because brains is obviously one of the organs in which humans differ the most, but I don't think that gives you the right to call a bunch of essentially truthful statements "bullshit."
Interesting speculations, but you're mistaken about the state of and techniques of the field of neuroscience:
"Hard to know for sure until we get the computational neurobiology tools in place to be able to strictly test such things"
You can't model mathematically (i.e. computationally) a thing you don't know. The problem is that we just don't know the topology of brain's neurons well enough to take a stab at making biologically accurate models. It is (should be) telling that the term used in cognitive neuroscience to describe experimental topologies is "biologically plausible" and _not_ "biologically accurate".
Neuroscience is still bottlenecked at the observation stage of the scientific process. We just don't have the capability of maintaining the state of more than about 100-200 neurons in situ, and they're all more or less topologically adjacent, even if the experimenter chooses judiciously. You can't formulate an "accurate" model of anything if you've only seen about one part in the ~10^9 parts that compose it. You can only make "plausible" models that are consistent with your few observations. Despite our very clever and even insightful efforts, the science of neurobiology is just too young to bear the ripe fruit we want in cognition and neuroscience in general.
If you're interested, I would encourage you (and anyone else) to read _recent_ literature in the field. Pick carefully, because some is dry, some is outdated, and some is unaccredited (for good reason!) slop hawked in popular publications by dilettantes like Jeff Hawkins of Numenta infamy.
The scientific process requires speculation, but it also requires evidence and careful analysis; it's been called "disciplined imagination" by some of its better-spoken proponents.
The neurons might still be flexible, but the adult mind isn't.
"These side branches also failed to make conventional connections, or synapses" and "Linden thinks they may present a second mechanism for conveying information beyond traditional synapses or assist in nerve regeneration, quickly forming synapses should nearby nerves get damaged."
That pretty much says it: they just sit there and do nothing but replace good ones.
Or they really think it provides a "second mechanism for conveying information beyond traditional synapses" -- but how can it convey information if it's not making "synapses", i.e. connections??? (And aren't synapses the way information is transmitted?) You have to "convey" information somewhere.
Or did they really have a machine that can see "traditional synapses" but can't see "non-traditional synapses"? It's a physical connection, right? How can you see one kind and not the other if you see the potential components of the connection?
That kind of conclusion is totally unwarranted. To begin with, the mice were not 70 years old. No, don't laugh! Either mouse neurons age as fast as the mice themselves do, which implies that (the processes in) their neurons differ fundamentally from ours, or these neurons age the way we do, but then they were studying two year old neurons, which I thought used to be considered pretty young.
Second, the observation that learning and memorizing becomes more difficult with age is pretty solid. If our neurons maintain their plasticity, these people should explain how a plastic brain stops learning.
Concluding: the observations are probably true, the conclusions were just made to draw attention and get more funding (aging is a big topic for funds these days). Such is the sad state of science.
PS I hold a post-doc in neurocognition.
Yes, his objection to the question of whether this research in mice is relevant to humans is bullshit. Period.
When have mice become people? I know we are very similar to rodents some more than others(like in congress), but since when are humans the same as rats, some things are bound to be different this could be one of them.
It has always been known that the brain continuously adapts (but for this it needs to be stimulated all the time, "use it or lose it" as they say). But my understanding at least has been that for this the brain signals travel along new paths of existing neurons to do brain functions. This is the first time I hear of proof that the brain also physically adapts. And at quite a high rate as well. This research could be especially important for understanding Parkinson's disease.
There is nothing on google other than this comment about "lei topographies" or "entropic topography".
If Google really cared they would fix Android Chrome to reflow text, instead of discriminating
As there seems to be some neuroscientists and neurologists on /., I'd like to ask the following question as its a somewhat related topic.
There is a man in his early 20's who recently recited pi to some 200,000 digits perfectly at Oxford university. He says he can visualize numbers in his head and is able to (as Oxford researchers found) do division to a precision of 20 or more decimal places in his head (there are some techniques to do this too I'm sure).
The point is he's said that his ability to visualize numbers occurred after having an epileptic seizure. After being diagnosed and (presumably treated with medication), his brain still functions in the same way that he's able to visualize the numbers.
Prior to being diagnosed with my seizure condition, I remember having epileptic episodes (the disorientation and spatial loss) where I was able to do more complex math and deeper thoughts that I ever thought I was able to do. As a senior in HS, I was able to complete math and science homework for sometime in a fraction of the time it would usually take. E.g. 25 minutes total vs 1-2 hours total each night. I haven't been able to unlock this thought process since.
Any thoughts or ideas on what caused this? And -without- any risk to myself, is there any current research on unlocking this potential?
This isn't progress. It is simply quantification. If you have ever worked with the disabled and physically/psychically traumatized you might wonder why exactly scientists wouldn't believe the brain to be more flexible "than they previously thought". The brain is so poorly understood in terms of how it works expect a long and tedious continuation of these pronouncements in the coming decades.
"Consensus" in science is _always_ a political construct.
If so, congrats! Your brain is not rigid yet.
Will you play?
That is essentially how you know if the state of your brain matters or not.
Blogging because I can...
Just ask the zombies.
I have excellent Karma and I am not afraid to Troll it.
Brains aren't flexible; they're squishy! You people should have learned this in high school biology lab... or was I the only one who dissected the pig's head for extra credit?
"Old age and treachery will win over youth and vigor every time."
Chas - The one, the only.
THANK GOD!!!
I'll believe them when they get me to speak Mandarin.
"Screw Sun, cross-platform will never work. Let's move on and steal the Java language." - Visual J++ Product Manager
Hi again.
I wholeheartedly agree that computational neuroscience is an exciting field with lots of potential. Indeed, as you assumed in your previous post, "if you can build [i.e. model mathematically] it, then you know it"; and that certainly applies to the field.
Neural network modeling (the mathematical theory part of this issue) are a bottom-up approach, while experimental neuroscience as a top-down approach. The former tries to figure out the forest by examining possible trees and groups of possible trees, and the latter tries to figure out the tree by examining extant forests. I'm not sure where you are in your course of learning, but I would identify 3 broad areas that I think you could probably (surprisingly enough) treat sequentially with some success:
1) A broad and hopefully moderately deep understanding of neural network theory including topologies and learning techniques
Read lots, play lots with both *pen/paper* and computer programming application. "Parallel Distributed Processing" by McClelland and Rumelhart is a cornerstone and I think the the two volumes' software is available on their site. Yes, it's a bit of a misnomer because their ideas are not really what the field of computer science would consider either "parallel" or "distributed", and the physiological speculation is a bit dated in a fast-moving field, but the basics are there. You'll need tons of "tools" to work with, from simple sequential networks to more complex recurrent networks, pattern recognition techniques, and thus a healthy tolerance for the precise mathematical analysis of these problems. For someone versed in classical computer hardware, there's the occasional analogy with combinatoric circuits, sequential circuits, and the like (but not so much with Von Neumann's memory); just don't go overboard.
2) With the basics in hand, time to look at the behaviors people have observed, and also the models people are working on. This is broad enough that there is always someone coming up with previously unexplored arrangements of the basic theories, even if the topological arrangements of their networks are relatively similar. You seem to be somewhere in (1) and (2) at the same time, which is fine and a more interesting way of learning, but I suspect the stereotype of computational neuroscientists as "scruffy" comes from people running before they can walk, or running without bothering to tie their shoelaces. With regard to your specific question, I think it is asked on new legs, and you would be interested in this if you are comfortable with its underpinnings and style:
"Cascade Models of Synaptically Stored Memories" (Fusi/Drew/Abbott)
I asked google and this is an easy place to read it (not my site):
http://neurotheory.columbia.edu/~larry/FusiNeuron05.pdf
There are other relevant (or at least interesting) things in that directory.
3) Cognitive neuroscience. It's a bit more experimental/observational than the other, theoretical, areas. For my little ternary classification, I'm grouping the extremely wide field of computational linguistics in here instead of in (2) because it has a particularly direct association with the observed phenomenon of natural language. Behavioral and physiological study, especially of non-healthy or damaged/lesioned brains is of course not really the same thing as the modeling you're interested in (we are the same in that regard, incidentally).
Computational linguistics (and machine learning/AI in general) requires such breadth and depth of knowledge that it's (IMO) the most interesting aspect of cognition after logic itself; it encompasses logic, and other neat things. There is TONS of relevant reading here, though it's only sort of browsable:
http://www.aclweb.org/anthology-new/
You'll find one paper that interests you, exhaust every leaf in its citation tree (web), and have enough ideas to jump to any other tree. It's enough for serious academic study, and way more than you'll need to get your feet wet.
Good luck and happy reading, programming, and whatever else you decide.
There are horrible blends of statistical fallacies here. It's like taking a simple average of peaks & valleys.
... will remember their cell number.
Group 1: Young People able to remember phone numbers:
These are the "Connected youngsters" talked about in conjunction with the rise of Web 2.0 and later, business networking. Someone constantly telling people to "call me on my cell"
Group 2: Older People unable to remember phone numbers:
Watch what happens when such a person is either not used to their cell, or moves to a new city and can't rely on the memory aid of the same area code. If they're unlucky enough not to get an easily memorable phone number, I've seen it take them months to learn it.
As for relative birthdays, it might depend on the cultural connectedness of the family. I may not remember Aunt Mabel's birthday because I never see her, but a young member of a family who "always has the family dinner every week" probably would because it would be about a "person in everyday life" and not a random factoid only needed once a year.
My first Journal Entry ever, in 8 years! http://slashdot.org/journal/365947/aphelion-scifi-fantasy-horror-poetry-webzine
The not-so-developed ones are tastier though. Lots of fat between the neurons makes for good eatin'!
Anyone interested in it should read The Brain the Changes Itself by Norman Doige; utterly fascinating and worth reading.
I can't think of any person in recent history who's more of a complete opposite of a nationalist-socialist / fascist than Ron Paul! I wish I could challenge whoever wrote that to a duel!