31–40 of 43 posts
Any idea on how this would affect learning with a spaced repetition software? Perhaps, the practice of excessive recalling with, say, Anki could essentially be detrimental to learning in some aspects? As it would make certain connections in a neural network unnaturally strong and cause saturation and overactivation in the last layer.
"The critical brain hypothesis suggests that neural networks do their best work when connections are not too weak or too strong." Isn't this just about as obvious as the fact that traffic flows best when traffic lights are neither always red nor always green?
The man compared it to playing chess with someone with your rating or a rating slightly up or down from it. Much more engaging than playing a CHESS GOD, or a totally first time player.
Earlier quoted context omitted.
"Critical" has a precise meaning in these kinds of systems; it essentially means when correlation lengths diverge (or, with a finite brain, become the size of the whole). In physical systems this happens at 2nd order phase transitions. Unfortunately most familiar phase transitions are first-order (boiling and freezing, for example) but the development of macroscopic magnetism as iron cools is an example. Away from th…
> "Critical" has a precise meaning in these kinds of systems; it essentially means when correlation lengths diverge (or, with a finite brain, become the size of the whole). If someone doesn't know what "critical" means, they also don't know what a "correlation length" is, so I don't think this clarification is very helpful. Who was the intended audience?
We don't just learn a new subject through simpler ELI5 explanations.
We also learn by immersing ourselves further into the subject (like here, were we were given an alternative, still elaborate explanation), until things "click".
In immersive learning (like how kids learn language and most other wordly things naturally outside of explicit teaching) we also get to understand the meaning of an unknown term by compounding other unknown terms, and making correlations, connections, and deductions.
"The critical brain hypothesis suggests that neural networks do their best work when connections are not too weak or too strong." Isn't this just about as obvious as the fact that traffic flows best when traffic lights are neither always red nor always green?
To me the fact that more information is transmitted with an intermediate number of connections than with a strongly connected network wasn't immediately obvious at first glance. I guess there is a link to entropy, i.e. how surprised can you be by the information received at one end of the network given its connectivity.
"The critical brain hypothesis suggests that neural networks do their best work when connections are not too weak or too strong." Isn't this just about as obvious as the fact that traffic flows best when traffic lights are neither always red nor always green?
Not really. Signals have a finite power level. If you open all the lanes all the time, you'll get a very attenuated signal throughout the entire network. If some connections are stronger than others, that's when you can actually see interesting behavior.
Earlier quoted context omitted.
I had a similar issue with the article. Essentially the information content seems to boil down to "there is a state where the brain works the best". For experts there is probably a lot to learn from the technicalities of this research, but the article leaves a layman a bit cold.
Try shaking your head then rereading the article, perhaps it will get better, or worse?
Earlier quoted context omitted.
"Critical" has a precise meaning in these kinds of systems; it essentially means when correlation lengths diverge (or, with a finite brain, become the size of the whole). In physical systems this happens at 2nd order phase transitions. Unfortunately most familiar phase transitions are first-order (boiling and freezing, for example) but the development of macroscopic magnetism as iron cools is an example. Away from th…
> "Critical" has a precise meaning in these kinds of systems; it essentially means when correlation lengths diverge (or, with a finite brain, become the size of the whole). If someone doesn't know what "critical" means, they also don't know what a "correlation length" is, so I don't think this clarification is very helpful. Who was the intended audience?
> Who was the intended audience?
From that definition? Obviously physicists, lol
Earlier quoted context omitted.
It's not a tautology, because it isn't clear that there is some threshold beyond which connections are too weak or too strong. I might think that more connections are more good, for instance.
It's not pretty https://en.wikipedia.org/wiki/Synaptopathy Imagine any dinner conversation in America could enter into any other dinner conversation as desired - it would probably saturate into a meaningless cacophony and all relevance and context would get lost.
If you look for something in a complex system, and you look hard enough, you're probably going to find it. The example of epilepsy might just be seeing certain behavior through the lens of the theory. Unfortunately, the article fails to give us any hard definition of criticality.
https://en.wikipedia.org/wiki/Ramsey_theory
> Problems in Ramsey theory typically ask a question of the form: "how big must some structure be to guarantee that a particular property holds?"