[HN Gopher] Critical brain hypothesis: A physical theory for whe...
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       Critical brain hypothesis: A physical theory for when the brain
       performs best
        
       Author : blegh
       Score  : 63 points
       Date   : 2023-01-31 19:58 UTC (3 hours ago)
        
 (HTM) web link (www.quantamagazine.org)
 (TXT) w3m dump (www.quantamagazine.org)
        
       | amelius wrote:
       | "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?
        
         | 0xcafefood wrote:
         | "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."
        
           | TchoBeer wrote:
           | 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.
        
             | 0xcafefood wrote:
             | So it might not be possible to reach a state with
             | connections that are "too strong" but logically if you
             | could you'd have to define them by suboptimal performance.
        
         | actually_a_dog wrote:
         | 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.
        
         | karmakurtisaani wrote:
         | 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.
        
         | anonymousDan wrote:
         | 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.
        
         | asplake wrote:
         | That makes it sound like optimising. To my not very great
         | understanding, I think it's more like keeping things right on
         | the edge
        
       | tgv wrote:
       | 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.
        
         | mach1ne wrote:
         | Amen. I think the generosity of complex systems for different
         | interpretations is the bane of comprehensive model for
         | neuroscience.
        
       | quantum_mcts wrote:
       | "The Principles of Deep Learning" paper
       | https://arxiv.org/abs/2106.10165 has a rather rigorous (based on
       | Quantum Field Theory (QFT) mathematical apparatus) analysis of
       | modern deep learning with the similar insight. They suggest that
       | the learning happens in the critical regimes. And use running
       | couplings, renormalization group and other fancy OFT math to
       | derive some insights in the DL field. Here's a HN thread, by the
       | way, https://news.ycombinator.com/item?id=31051540.
        
       | DecayingOrganic wrote:
       | 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.
        
       | gardenfelder wrote:
       | Stuart Kauffman explored this idea years ago with his NK Theory
       | [1]
       | 
       | [1] https://en.wikipedia.org/wiki/NK_model
        
       | varjag wrote:
       | Wonder if there's a connection with Ballmer Peak.
        
         | vermilingua wrote:
         | I doubt Ballmer Peak needs to reach as far down as neuroscience
         | to find a basis: developers tend to overthink, and alcohol de-
         | thinks.
        
           | alexpotato wrote:
           | The book Drunk spends a lot of time on this very point:
           | 
           | From a long term health perspective, alcohol is very bad or
           | even toxic for humans. That being said, it's been used
           | throughout history for short term benefits ranging from
           | creativity to facilitating social interactions. On the
           | creativity side, the author explicitly states how drinking
           | alcohol can help adults activate child like curiosity and
           | thinking. The idea being that the adult mind has too many
           | inhibitions and the alcohol helps lower those for both
           | internal and external scenarios.
           | 
           | 0 - https://www.amazon.com/Drunk-Sipped-Danced-Stumbled-
           | Civiliza...
        
       | revskill wrote:
       | To me, it's always after sleep.
        
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       (page generated 2023-01-31 23:00 UTC)