Neurons in the brain appear to follow a distinct mathematical pattern(msn.com)
msn.com
Neurons in the brain appear to follow a distinct mathematical pattern
https://www.msn.com/en-us/health/other/neurons-in-the-brain-appear-to-follow-a-distinct-mathematical-pattern/ar-AA1mhCzO
12 comments
Dumb question but on mobile, how do I actually read the article? All I see is the abstract and a bunch of MSN spam.
It seems to be a syndication of this post, maybe this renders better for you:
https://www.sciencealert.com/neurons-in-the-brain-appear-to-...
Also maybe best to change the article link to that one.
Also maybe best to change the article link to that one.
I was also stumped by this. Then found the "Expand article" button.
No comment on how significant this is, but here's a link to the actual research: https://academic.oup.com/cercor/article/33/16/9439/7219677
and here's the announcement by the Human Brain Project: https://www.humanbrainproject.eu/en/follow-hbp/news/2023/08/...
Thinking about how this may apply to AI, this research seems to be saying that the distribution of neurons is not a normal distribution, but more akin to the population of a country, where there are dense clusters of neurons amid a mostly sparse network, the same way populations heavily cluster in urban areas.
We don’t yet know the reason for that structure, but it appears across species and across different regions of the brain
It could be that a similarly modeled neuronal network would exhibit more pockets of specialized activity in response to certain inputs, and potentially even redundancies in specialization allowing it to process the inputs in slightly different ways to reach ensemble/consensus style outcomes. In some ways it sounds reminiscent of the mixture of experts approach
It’s certainly interesting!
We don’t yet know the reason for that structure, but it appears across species and across different regions of the brain
It could be that a similarly modeled neuronal network would exhibit more pockets of specialized activity in response to certain inputs, and potentially even redundancies in specialization allowing it to process the inputs in slightly different ways to reach ensemble/consensus style outcomes. In some ways it sounds reminiscent of the mixture of experts approach
It’s certainly interesting!
There are many statistical distributions and power laws in nature. The distance between neurons doesn’t have to follow a normal distribution any more than it could follow the long tail distribution. The other common distributions are often overlooked by fixation on bell curves in data analysis.
Anyways, this tendency seems to continue throughout the body in neural nexuses. And even outside the body in how we communicate our thoughts in social circles.
I also wonder if language itself is deeply related to this structure. Zipf’s law relates to the long tail. Semantic space models also often show clusters of words and symbols. We have synonyms for most symbols we speak about, and often antonyms, but rarely are we able to think about something that’s about in the middle. But a LLM could have parameters such that it would produce semantically unrelated words “in the middle” if it was poorly fitted. In other words, if it was bad at producing language. So clearly, there is a property of at least language-based neural nets that they are necessarily related to the long tail probability distributions.
Perhaps many more things than we commonly think are on a long tail distribution curve when it comes to neural nets, real brains, and our society. If only the normal distribution wasn’t so intoxicating to the academia and we paid other probability distributions the same amount of attention…
Anyways, this tendency seems to continue throughout the body in neural nexuses. And even outside the body in how we communicate our thoughts in social circles.
I also wonder if language itself is deeply related to this structure. Zipf’s law relates to the long tail. Semantic space models also often show clusters of words and symbols. We have synonyms for most symbols we speak about, and often antonyms, but rarely are we able to think about something that’s about in the middle. But a LLM could have parameters such that it would produce semantically unrelated words “in the middle” if it was poorly fitted. In other words, if it was bad at producing language. So clearly, there is a property of at least language-based neural nets that they are necessarily related to the long tail probability distributions.
Perhaps many more things than we commonly think are on a long tail distribution curve when it comes to neural nets, real brains, and our society. If only the normal distribution wasn’t so intoxicating to the academia and we paid other probability distributions the same amount of attention…
It would be interesting to see if these clusters are related to resource distribution (blood vessels) like how cities often grew up around rivers
Sorry if this is a dumb question, but if neurons are clustered together like humans do, what's in the spaces where there are no neurons, the not populated area? Is it fat or maybe water?
To link back to a previous post from 7 months ago:
https://medicalxpress.com/news/2023-08-mathematical-neurons-...
https://news.ycombinator.com/item?id=37234758
Microsoft wants me to install the Start app on my Android tablet to read this. I decline.
Microsoft tries so hard haha
It's sort of getting out of hand tbh. I mean, look at microsoft edge. To set another browser as your default browser on windows, you need to change a mass amount of settings and edge will still find a way to open!
It's sort of getting out of hand tbh. I mean, look at microsoft edge. To set another browser as your default browser on windows, you need to change a mass amount of settings and edge will still find a way to open!