21–30 of 43 posts
"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?
Earlier quoted context omitted.
"The critical brain hypothesis suggests that neural networks do their best work when connections are not too weak or too strong." is actually a tautology, no? Without a crisp explanation for what "too weak" or "too strong" mean, this is just saying "Neural networks work best when connections couldn't be changed to make them work better."
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.
"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?
Away from the critical point the dynamics become either 1) too strong, meaning that information has a hard time getting from one spot to another because it's in too much traffic 2) too weak, meaning that information has trouble being processed, because the traffic is so light that cars don't spend enough time together to come to equilibrium
In these cases the correlations will be short-distance, in contrast to critical.
Wonder if there's a connection with Ballmer Peak.
I doubt Ballmer Peak needs to reach as far down as neuroscience to find a basis: developers tend to overthink, and alcohol de-thinks.
"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?
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.
"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?
"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…
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?
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?
Presumably the “correlation length” is something kind of like “some length where there is much larger correlation of pairs of things at most this length apart, than there is between pairs of things that are much longer than this length” (but that’s largely just a guess)
"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?
I think this is somewhat related to criticality in nuclear fission in nuclear reactors - if a reactor is sub-critical, then any reaction fizzles out; if it is super-critical, then the reaction is exponential. If the reactor is close to the critical point, then the reaction is sustained at a relatively constant rate.
The brain probably couldn't function very well if their neurons were generally super-critical and all fire very quickly if one fires. And the brain probably wouldn't function well if the neurons were all sub-critical, and any attempt at transmitting a signal would die out.
The researchers studying this critical point theory for the brain also seem to be seeing parallels in brain activity and other critical systems. Particularly, that a system at a critical point can be easily tipped over the critical point with a small input or simply a change in one of its components. And that some systems in nature seem to use this to stay organised.
The hypothesis is very cool and seems to be sensible, but the article and video depart a bit too much into adjacent topics to offer a good introduction to the subject. For example, I couldn't find the relevance of the critical point between random and organised systems in metal atoms. The example of seeing predators in nature was also a bit contrived although it illustrates how a small change in a large neural net can produce large meaningful organisation in the activation of neurons.
It feels very intuitive that a brain needs to operate at this perfect balance between super-criticality and sub-criticality, I can understand why some scientists feel so strongly about it.
I would welcome corrections if I did not understand something as intended.
Earlier quoted context omitted.
"The critical brain hypothesis suggests that neural networks do their best work when connections are not too weak or too strong." is actually a tautology, no? Without a crisp explanation for what "too weak" or "too strong" mean, this is just saying "Neural networks work best when connections couldn't be changed to make them work better."
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.
Even still, there are a finite number of nodes, so there is a max number of connections. So the "just right" amount could be everything connected to everything, and the statement would still be true.
Earlier quoted context omitted.
"The critical brain hypothesis suggests that neural networks do their best work when connections are not too weak or too strong." is actually a tautology, no? Without a crisp explanation for what "too weak" or "too strong" mean, this is just saying "Neural networks work best when connections couldn't be changed to make them work better."
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.
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.