Book Review: Rhythms of the Brain | Hacker News Reader

Book Review: Rhythms of the Brain

astralcodexten.substack.com

108 pointsfeross18 comments

Gyorgy Buzsaki is one of the least appreciated and most profound neuroscientists alive. Like many proper philosophers, I think his understanding is best appreciated in conversation as opposed to via lectures or books. Though it is the case that his ~1982 review of hippocampal oscillations (which he basically codified) predicts a large majority of the concepts that neuroscientists that study sleep, learning, memory, and decision making are still trying to prove.

What makes you say he is underappreciated? He is a rock star in neuroscience

I'm reading through this right now actually! The text is available for free here: https://neurophysics.ucsd.edu/courses/physics_171/Buzsaki%20...

If you are interested in the brain as an oscillatory system, you may appreciate the rigor with which we clarified key concepts around “resonance” in the brain.

“Resonance as a Design Strategy for AI and Social Robots” https://www.frontiersin.org/articles/10.3389/fnbot.2022.8504...

Also, this paper is a systematic review of the concept of harmony. The section on neuroscience reviews evidence for why brainwaves are structured like octaves (frequency doublings), viz, to support phase-amplitude coupling.

https://doi.org/10.1016/j.sheji.2022.01.001

Looks great; bookmarked. Was expecting to see another paper that talks about resonance without any mention Stephen Grossberg's seminal work, but great to see that you do cite him! :) And his 2017 paper at that, which I imagine will become one of his most celebrated.

https://www.sciencedirect.com/science/article/pii/S089360801...

I'll add that his work is not just about how resonance enables adaptive learning, but how resonance enables adaptive learning via conscious mechanisms. Grossberg was not only the earliest to suggest how resonance might play a role in consciousness, but also the only one to offer a mechanistically precise theory of consciousness that also has great explanatory power. (I am contrasting this with other consciousness theories like IIT and global workspace)

More here: https://resonantbrain.substack.com/p/understanding-conscious...

I really liked your diagram in the blog post. Very clear. (And, I preordered your book!)

We recently proposed an evolutionary theory of consciousness — published by Darwin College at Cambridge. You might find it interesting, as well. https://www.cambridge.org/core/books/enigmas/enigma-of-mind-...

Thank you! (our book is already out; hope you didn't order the paperback that comes out early next year)

Look forward to reading the chapter and the book, after I finish your article :)

Thanks for your article, it is very interesting.

I don't necessarily see the difference between the very "slow" statistical feedback loop of cars and the equivalent in consciousness: what if a brain the size of a galaxy existed - such that signals across it took thousands of years. Therefore changes would appear anything but "real-time" to an observer on a vastly different timescale. Would that then mean the entity cannot be considered conscious? In which case, when would we arbitrarily drawing the line - at a relative distance from ourselves? Likewise on small scales, if a self-correcting FPGA was rewiring itself at a scale and speed we cannot perceive, would it not quality for consciousness simply because of our egos?

It's also worth noting that a possible alteration could be made to the definition you make of consciousness:

> Consciousness is the constellation of past experiences experiencing the present, assimilating it to act and prepare for future opportunities.

What we commonly believe to be conscious beings don't prepare for the future, they prepare for trajectories towards imagined futures that are maximizing internal criteria. We have no evidence that conscious beings have any capability to hold the entire causality of reality within their grasp before the dice are thrown, therefore the past really is the entirety. Some of it informs our classification of "past" things (clear memories of childhood for example), and all of it contributes to the shape of internal models we use to model things, predict & estimate outcomes, and pick maximization/minimization actions.

Basic question for people in this field: have a Muse S (2nd gen). Are this device sensors enough to play with more serious research/biofeedback that the features included in the Muse app?

I understand the device includes a developer SDK. I didn't find enough feedback or usefulness with the official app. Don't want to troll but personally after an hour of running I feel great while using the different options in the app I don't feel nothing special.

I strongly recommend his next book, "The Brain From Inside Out". While Rhythms of the Brain is indeed aimed at neuroscientists, and is pretty heavy reading, Inside Out seems to be from more of a hacker's perspective.

They're good compliments to each other, but I found Inside Out to be a bit more actionable.

Could you share some actionable advice from Inside Out?

I'm not completely though it yet, but I find the graph theory aspects of the brain to be really interesting. It turns out that the vast majority of neurons are connected to their immediate neighbors (unsurprising), but the minimum path length between any two neurons is significantly shortened by a very small number of "distant" connections.

Which leads me to wonder about variations on Conway's Game of Life, where specific random cells have "neighbors" that are spatially distant, and what the dynamics of that sort of system would look like.

This is a small world network. I'm sure GoL on small world networks has been done to death

Actually I found Inside out a bit hard even though I'm a computational neuroscientist like Buzsaki. Or maybe not really hard, but a bit repetitive and not so engaging.

It's becoming clear that with all the brain and consciousness theories out there, the proof will be in the pudding. By this I mean, can any particular theory be used to create a human adult level conscious machine. My bet is on the late Gerald Edelman's Extended Theory of Neuronal Group Selection. The lead group in robotics based on this theory is the Neurorobotics Lab at UC at Irvine. Dr. Edelman distinguished between primary consciousness, which came first in evolution, and that humans share with other conscious animals, and higher order consciousness, which came to only humans with the acquisition of language. A machine with primary consciousness will probably have to come first.

What I find special about the TNGS is the Darwin series of automata created at the Neurosciences Institute by Dr. Edelman and his colleagues in the 1990's and 2000's. These machines perform in the real world, not in a restricted simulated world, and display convincing physical behavior indicative of higher psychological functions necessary for consciousness, such as perceptual categorization, memory, and learning. They are based on realistic models of the parts of the biological brain that the theory claims subserve these functions. The extended TNGS allows for the emergence of consciousness based only on further evolutionary development of the brain areas responsible for these functions, in a parsimonious way. No other research I've encountered is anywhere near as convincing.

I post because on almost every video and article about the brain and consciousness that I encounter, the attitude seems to be that we still know next to nothing about how the brain and consciousness work; that there's lots of data but no unifying theory. I believe the extended TNGS is that theory. My motivation is to keep that theory in front of the public. And obviously, I consider it the route to a truly conscious machine, primary and higher-order.

My advice to people who want to create a conscious machine is to seriously ground themselves in the extended TNGS and the Darwin automata first, and proceed from there, by applying to Jeff Krichmar's lab at UC Irvine, possibly. Dr. Edelman's roadmap to a conscious machine is at https://arxiv.org/abs/2105.10461

We build on Edelman’s neuronal group selection theory and connect it to an oscillatory framework in this chapter published by Darwin College. It is the most fun work I’ve ever written.

https://www.cambridge.org/core/books/enigmas/enigma-of-mind-...

We connect this to robotics in a separate paper. The big question for me is in the value of coupled oscillators as a computational framework. Notably, von Neumann posthumously patented an oscillatory computer architecture. I’m really hoping to develop this further in the future.

I find it a little confusing that the author compares 1/f noise from the brain (electrical) with acoustic pink noise, which is a different type of wave altogether. Am I missing something or is it just a poor analogy?

Not read it, but 1/f frequency distribution is pink noise. Is your point that the origin of the oscillations is different? It is natural to wonder about common causes for common patterns no? Eg thermodynamics and black holes. So doesn't necessarily seem to be a poor analogy

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