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What I'm interested in regarding synchronized brain waves are interferences. I suspect that consciousness is the ability of humans to coordinate brain functions in such a way that they "compose" higher order patterns of brain waves that interfere with each other, creating the brain wave equivalent of a moiré pattern. When we evolved conscious minds, we basically learned to create those patterns.

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Scott, you wrote in your review of MCTB that:

> Ingram notes that every sensation vibrates in and out of consciousness at a rate of between five and forty vibrations per second, sometimes speeding up or slowing down depending on your mental state. I’m a pathetic meditator and about as far from enlightenment as anybody in this world, but with enough focus even I have been able to confirm this to be true. And this is pretty close to the frequency of brain waves, which seems like a pretty interesting coincidence.

Can you go into any more detail about what it's like to experience sensations in this discrete way? Also, kinda off-topic, but what's up with the jhanas? If anyone can experience mega-bliss states with a few months of proper training, shouldn't that be, like, a major EA cause area?

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Event-related potentials (ERPs) are like single-shot brain waves that occur in response to stimuli; had to take a whole section on them in grad school (with the guy who wrote the ERP textbook), and I still was not sure how they were supposed to be important. Most of it was used in research on attention (e.g., this spike would occur if you noticed X but not Y); I think they are supposed to be informative because you can measure them without having the subject respond at all (no button pressing responses, just pure noticing). At least they don't bring out some of the "woo" or just speculation like brainwaves tend to do. Was it Crick & Koch who thought consciousness was (possibly) a 60 hz oscillation of the frontal cortex or something like that?

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In my undergrad biology program we visited a brain research lab near Moscow. The brain scientist gave us a brief intro to Fourier transforms, which made me understand how beautiful they are - something that 2 years of undergrad math classes didn't manage to do.

Then he explained the brain waves to us like this:

"Imagine you are standing outside the football stadium. You don't see what's happening inside, but you hear the chatter of the crowd. All the individual words blend together into indistinct mess and although there's definitely a local information transfer going on, from the outside you can't make out anything specific.

Then imagine one of the teams scored a goal. The crowd behavior is now very different! The fans of the winning team start to cheer and sing. You can easily pick this up from outside and infer what's happening. This is because the individuals behave in a globally coordinated manner, so their signals amplify each other in tune.

From this perspective, brain waves are a byproduct of globally coordinated neuronal activity, and it's the first one we historically learned to pick up. They appear when neurons stop chatting with each other and start chanting in unison."

Then he plopped some probes on my head and announced I have beautiful epileptic spikes (I'm not an epileptic)

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Oct 20, 2022·edited Oct 20, 2022

I was in a workshop with some people who studied "Grandmother neurons." The story is that a particular neuron only fires when you see your grandmother. This is a false. The true story goes something like this: Imagine encoding memories as combinations of 88 piano keys (neurons). If you used a binary encoding, you could store 2^88 memories, but if you got one note wrong, it would be a totally different memory. If you stored one memory per key, you could only store 88 memories. The brain actually uses sparse codes-- a memory encoded as a chord, only using up to 10 keys played at once (since we only have 10 fingers) This can store "88 choose 10" which is about 4 billion memories. So when the scientists measure a hundred randomly sampled neurons, it just so happens that one particular neuron only participates in the chord for the grandmother in the tests. Does it participate in other chords? Yes, but they are all for images the scientist didn't test. Are there other cells involved in the chord for the grandmother? Yes, but the scientists didn't stick a measuring device next to those neurons (it's hard to do and if you stick in too many probes they damage the tissue).

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Arguably, even AI models have "waves". It is just that with the currently dominant paradigm of transformer models there is only a single wave going from input to output and no oscillations or recurrences. This used to be different, recurrent models like LSTMs, Neural Turing Machines and other older ideas could be described as oscillating. It just turned out that these recurrent models are hard to train with current methods and to scale to large amounts of data. Interestingly, recurrent models are thought to be better at reasoning tasks and algorithmic learning, something that transformer models are bad at ("bad" here meaning in comparison to their other skills like memorization and language understanding).

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Brain waves are weird to me. I think of neurons as having certain jobs. Let other neurons know when there's a stimulus, or when the sum of stimuli reach a thresh hold, or do whatever computation. How does it help if every now and then (by which I guess I mean ~7 times a second or whatever) they all throw that calculation away and do the wave? Brainwaves seem to me like having the music on real loud in a party and trying to hold a serious conversation at the same time. But... music is pretty much ubiquitous at parties, so maybe it's actually a good analogy?

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Thanks I was considering whether or not to order this just the other night, and this review has tipped me into going for it.

Here's an interview about his latest book - which seems to get at the idea that the brain is a self-coherent system, which is stable in and of itself without needing to be "driven" by sensory events. Anyway the summary can do a better job than me, for the lazy its all summarised from about 1:02


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I love the examples here. The concept of “a stimulus is only detectable if it hits at the wave Peak” made me think of some mechanism for tuning out annoying sounds.

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Minor typo - in the section talking about waves of different frequencies getting into sync, you’ve got the frequencies backward. The 2.1 Hz wave will prompt the 2Hz one to fire early, and the 4.1 Hz wave will prompt the 2Hz one to fire early on its second pass, rather than the other way around. (Everyone always gets wavelength and frequency reversed!)

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You've hit on my favorite topic! I too found his stuff on pink noise difficult to parse.

Brain waves become significantly more exciting when you do a deep dive into specific oscillators.

1. Sharp-wave ripples (SWRs, 80-140 Hz) play an essential role in memory recall.

> Girardeau et al (2009) provides loss of function evidence that suppression of sleep SWRs dramatically impairs subsequent memory recall for spatial tasks. This finding is nicely complemented by several gain of function experiments. Fernandez-Ruiz et al (2019) were able to prolong ripple duration, and showed that prolongation improved performance, and conversely that shortening impaired performance. This may explain why novel situations naturally evince longer duration ripples.


https://www.youtube.com/watch?v=a8rbCJ8JSd0&t=41s There's a longer version of this video in supplementary materials of the paper.

2. The SPEAR model, which associates peak of theta wave with past encoding vs trough with future predictions is one of the most extraordinary facts I know.


3. The link between respiration-entrained rhythms and volition.


4. How gamma-beta implement feedforward and feedback signals (in shallow and deep layers of cortex respectively), fully consistent with active inference models. Very strong recommendation for Earl Miller's laboratory. https://www.youtube.com/watch?v=Kqyhr9fTUjs&t=1841s

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When I was an undergrad (1993) I had to lead a seminar on something for a senior CSC class. I chose chaos-and-neural-networks because there had recently been some intriguing brain research that purported to demonstrate that oscillations of neural activity were chaotic and developed attractors that corresponded to what the neural activity was "about" (the specific example had to do with the olfactory bulb of some animal and different scents). I speculated that maybe the course of artificial neural network research might change and become less linear (input neurons ↣ more layers ↣ output neurons) and more feedbacky (input neurons ↣ bunches of neurons feeding back to one another perpetually kind of like in those game-of-life animations).

Anyway: I bring this up because after graduation I completely dropped this thread and haven't kept up on it, and now I'm suddenly curious. Does this cognitions-as-chaotic-attractors thing still get taken seriously? Does anyone do anything with artificial neural networks that maintain a constantly, chaotically oscillating state rather than designating some "output neurons" that terminate the activity?

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Oct 20, 2022·edited Oct 20, 2022

> And the waves solve wetware-specific problems, like conduction delay (silicon chips operate at the speed of light) and synchronization (computers have internal clocks, or can synchronize with atomic clocks via the Internet).

Silicon chips definitely deal with conduction delay. The speed of light is very fast, but at 4 GHz electric signals (moving at roughly 2/3 the speed of light) can only travel 5 cm in one clock cycle. Traveling 1 cm uses 20% of the cycle, etc. Computers also have to deal with various types of synchronization issues for a variety of reasons.

However, the logic here was flawed from the start, because artificial neural networks are a mathematical model simulated on a computer and the properties of the computational substrate of a simulation don't carry through into the simulation. And so ANNs indeed do not have to deal with conduction delays and synchronization issues the same way that biological neural networks do, but the reason does not have anything to do with the low-level details of silicon chips.

In an ANN, there is simply a list of neurons, a record of which neurons are connected to which others, and an assignment of weights to these connections. There is normally not an assignment of lengths to the connections. Therefore there can be no conduction delay, for example. You could create a nonstandard ANN with lengths and simulated conduction delays if you wanted, as that freedom is the nature of simulation. The conduction delays in the wires within the silicon chip don't inherently create conduction delays in the ANN, because the connections in the ANN are not wires in the ANN, they are numerical data upon which the silicon chip performs calculations. Just like the flatness of the silicon chip doesn't prevent simulated 3D environments, etc.

This may seem piddly, but it's important to keep in mind that the properties of an ANN come from the properties of the mathematical model, not from the properties of wiring in silicon chips.

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Oct 20, 2022·edited Oct 20, 2022

As someone who knows nothing about the subject, my first reaction is: How do we know brain waves aren't just random noise to which we wrongly impute meaningful patterns? The post mentions that "pink noise" exists in stock market behavior. Aren't waves in the stock market the kind of things technicians love to write books about but which ultimately have no value for traders?

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Oct 20, 2022·edited Oct 20, 2022

AI have no brainwaves?

I beg you to consider the beautiful paper "could a neuroscientist understand a microprocessor" https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005268

Gems like the donkey-kong transistor vs the space-invaders transistor on the motorola 6502 (lesion the transistor, observe which behavior is impacted). Different switching rates and synchrony between chip regions across different behavior (switching transistor as analogue of firing neuron).

They didnt attach a real oscilloscope, but that would probably have uncovered beautiful chip waves, from the slow background 50 Hz (bleed from the power grid) to chip clock, memory clock, a variety of bus speeds, etc.

edit ps: speed of light is not so fast. 5 ghz means 6 cm, or about 3 x diameter of a chip. Brain analogue 50 m/s across 20 cm would be 80 Hz, so signal conduction in chips is quite slow compared brains, in relation to clock and distance.

pps. This always gets me when neuro people are like "the brain is so weird, it rolls back time and retcons stuff during saccades. an ai would never do this, bio sucks". Have you lived under a rock during spectre? Your phones cpu does this constantly. When you ask the cpu for its state it literally rolls back time and retcons a state it could have been in that is compatible with assembly level understanding. As the quip goes, the architectural state is architected.

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Super interesting post but raises so many questions!

> there are other even longer-period brain oscillations of unclear purpose.

What are these??

I understand that when neurons fire, by default they're firing at like 100Hz and 'increases in activity' are upticks in the rate. What's the relationship between these frequencies and brain-wave ones? Are some waves at this high frequency, and some of them talking about pulses of raised frequency?

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Oct 20, 2022·edited Oct 20, 2022

I wonder if creatures with very small/simple brains like jellyfish have brain waves?

Fun chart here but I guess it’s total neurons in body, not brain: https://en.wikipedia.org/wiki/List_of_animals_by_number_of_neurons

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Hey all. - I'm a working neuroscientist who's spent some time thinking through some of these issues.

I would say that there's a significant proportion of the field that is, shall we say, "skeptical" of the Buzsaki-style view on oscillations. Everyone agrees that the oscillations exist, and that changes in the oscillations are often correlated with changes in brain state, but making *causal* statements about their impact has proven much more difficult. There are certainly some suggestive data in the hippocampus pointing towards the importance of things like theta-band power and sharp wave ripples, but even there you get inconvenient data points like the fact that bats have a completely normal, functioning hippocampus with no apparent theta band oscillations: https://www.sciencedirect.com/science/article/pii/S0092867418312297

More broadly, it's hard to shake the feeling that the brain has some natural resonances for various states - sleep/unconsciousness is low frequency, low activity is ~alpha/beta, high activity is ~gamma. A statement like:

"Ask someone to think about a certain topic, and cells representing that topic will form a neuronal assembly (ie they will start oscillating together at the same frequency) in gamma rhythm."

is really just saying that those cells are active, and neurons in an active brain area tends to follow a gamma rhythm. In fact, I'm reasonably confident that if you were to look at the internal activity of cells in the same area that do NOT represent that topic, they would also be following the same gamma rhythm, just with less tonic input and thus less spiking activity.

People like to study oscillations because they're brain-wide phenomena that are (relatively) easy to detect in humans with non-invasive methods like EEG. However, anyone who's looked at neural activity using more finely-tuned, invasive methods (e.g. single neuron electrophysiology) knows that individual neurons close by each other can be tuned to very different sets of variables, and that when you average signals from large populations of neurons you lose a lot of useful information.

To be clear, I think everyone in the field agrees that neurons act together in assemblies, that patterns of activity that are shared across neural populations are important, and that sometimes these population patterns of activity can oscillate. The problem is that there are many, many, many of these subnetworks active at any given time, and that when you average them all together you get a signal that's easy to measure, but is limited in its ability to explain anything beyond basic brain function. You have 86 billion neurons in your brain. You should be skeptical of theories that tell you just 4-5 frequency bands are the secret to understanding it.

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Instead of 2.17, the golden ratio φ=1.618... is best for anti-resonance. This is because φ is maximally difficult to rationally approximate.

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If anyone is interested in learning more about the Fourier Transform (IMO one of the most beautiful concepts in mathematics), there's an incredible Stanford course on it: https://see.stanford.edu/course/ee261

I actually had a big "aha!" moment while meditating during the months I was taking the course. It's hard to describe, but it was like suddenly seeing my internal rhythms in the frequency domain instead of the time domain.

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> The hippocampus is traditionally associated with memory, but Buzsaki cites recent research showing that it also handles navigation (eg a rat navigating a maze, or a person walking through a familiar city). Why this combination of tasks? Rhythms sort of suggests that brain areas are less about specific tasks than about specific graph-theoretic arrangements, which are convenient for specific algorithms, which are convenient for specific tasks. The neural connections in the hippocampus are mostly local in a way that makes it a good “auto-associater”, ie you give it part of a pattern and it can recognize which pattern is being suggested. For complicated reasons this works well for both spatial tasks and memory. In fact, Buzsaki sketches out a way that episodic memory might have evolved from our navigation system, where eg remembering a list is equivalent to remembering a path through space, and remembering a specific episode is equivalent to remembering a landmark (he doesn’t specifically say so, but this is a fascinating match for the method of loci mnemonic device).

Also, changing location can make you forget things, the "doorway effect" .


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According to Wikipedia, pink noise is defined by the power *per frequency interval* being inversely proportional to the frequency - in other words, power *over time* is constant, independent of frequency. I don't see what's supposed to be mysterious about that, though. It makes perfect sense to me that the brain would want to maintain a consistent power draw, and would attenuate the amplitude of high-frequency signals to make that happen.

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> In fact, Buzsaki sketches out a way that episodic memory might have evolved from our navigation system

My episodic memory is terrible, and so is my navigation ability. In other words: put that on the survey!

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I think the only examples I will remember are the fake toy ones, because they are simple and memorable. And soon I will forget that they are fake and remember them as real. Well done!

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> Brain waves provide “synchrony”, allowing a smallest granular unit of time and essentially converting life into a turn-based game. Suppose that a snake bites your foot. You see the snake with your eyes, and also get a pain signal from your foot. The pain signal has to travel a long way, nerves have conduction delays, and so it reaches your brain well after the visual signal. But your brain needs to be able to combine the visual and pain signals into a single story (snake bit my foot). Brain waves separate experience into short granular “turns” so that the brain can attribute both stimuli to the same “turn” and connect them.

I'm having a hard time getting past the example. It's true that the brain needs a way of combining the visual signal and the pain signal into a single story. But there is quite obviously no need for synchrony or a minimum quantum of time at any point in this process; if I feel pain and then, five full minutes later, see a snake slither away from where I was standing when I got hurt, I'll still combine those two signals into "a snake bit my foot" (where that combination is plausible).

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Delays that need to be combined into a coherent whole is the entirety of multiplayer game network coding. People clicking buttons, sending them with variable delays along with a "happened at time t" attached to a server which then has to retroactively figure out who hit and who missed and then send that back in time. Ask game network engineers for a books worth of problems and solutions over the last 40 years :)

Games also use frames, discreet moments in time, to make physics and simulations doable at all. Too low a framerate and you notice the choppiness. Also if your monitor frequency and game frame frequency are nice multiples, it looks good, if not, it's choppy as well. Many analogues!

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Why not just say that brain waves are emergent? They don’t do anything in particular. They are just the result of something else that does something. So everything here will only be based on correlation, not causation.

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One thing that helped me understand EEG better, and I have seen other commenters mention is that there is a big difference between what the neurons are actually doing and why and what we pick up from the EEG with electrodes on your scalp. Each individual neuron fires and makes a tiny electromagnetic pulse sure, but if all the neurons were aligned randomly all these pulses would sum to zero - no signal. If the neurons all fired at random times they would sum to zero - no signal. If it's a small group of neurons it's too weak to make it to the scalp and overcome noise - no signal. So we only 'hear' large groups of aligned, synchronised neurons. So any sort of brain activity that doesn't meet these requirements is invisible to us unless we use electrodes to record the activity of single neurons. We also can't distinguish between synchronous activity being 'used' by the brain for a purpose (like synchronising areas of the brain), with byproducts of activity (like to actually move a muscle some amplification of a small signal has to take place to have a bunch of neurons fire at the same time).

This helps to explain the pink noise thing - larger groups of aligned neurons make a stronger signal, but neurons are slow so unless these areas are oscillating slowly its hard to get large areas to line up. So stronger signals tend to be lower frequency, average that out across millions of areas of different sizes and you get something like the pink noise curve.

I also can't stress enough how terrible a signal EEG is - it's the worst biosignal by far. Brain activity is already basically random noise with slightly different non-random noise overlaid in it, then its smeared out by volume conduction as it moves through the head, then it picks up every bit of environmental noise and muscular activity, then the electrodes and cables move and skin conductance changes, then its all screwed up by referencing and grounding effects. My research was in brain computer interfaces and my advice is stay well away until either portable MEG systems or intra-cortical electrodes go mainstream.

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Not too surprisingly, awareness appears to correlate more with overall signal diversity than specific wave frequencies:

"The foregoing studies complement an older, yet largely unacknowledged, body of literature that has documented awake, conscious patients with high amplitude delta oscillations in clinical reports. . . a largely parallel body of recent work has reported convincing evidence that the complexity or entropy of EEG and magnetoencephalogram or MEG signals strongly relates to an individual’s level of consciousness"

Hyper-synchronous activity correlates with unconscious states like deep sleep and generalized seizures.


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In Conway's Game of Life, people have made some patterns that act as computers. For example there's this pattern that calculates and prints the digits of π: https://conwaylife.com/wiki/Pi_calculator. Here's a plot of how many live cells it has over the first 8192000 generations: https://i.imgur.com/KyICAc0.png.

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In an analogy to the origin of the names of white, pink, and brown noise, the laboratory technique to separate and study DNA of different lengths is named southern blotting for its developer, Ed Southern. The similar method of separating RNA is called northern blotting, and for protein it's called western blotting. Once, I accidentally reversed the electrodes when doing a western blot, so that the protein ended up lost in the buffer instead of on the blot paper, which I guess could be an eastern blot. But apparently there are many things called eastern blots now: https://en.wikipedia.org/wiki/Eastern_blot

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I feel the speculation at the end doesn't take sufficient notice of the fact that we are observing the system from the inside. For instance, whether or not time *feels* discrete in meditation has no real necessary relation to how the brain actually processes events. You could easily imagine a brain which processes events in discrete blocks but where this blockiness was imperceptible to self-reflection. Alternatively, the brain could just process experiences as they came in so there was no discreteness but then, when you try to reflect on the experiences, batch them together in a way that feels discrete.

More generally, I don't like these grand attempts to explain meditation/enlightenment as they all seem to suffer from this kind of importance bias: since these states feel so important they must reflect important brain processes. But isn't the most likely explanation just that the brain is quite plastic and it shouldn't be that surprising that you can train/trick it into producing pleasureabld experiences?

But those experiences need not be caused by grand things like linking usually unconnected regions...but most likely by the same kind of fuckups and errors that drugs often work by just without the direct chemical intervention (so less vulnerable to tolerance).

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Thanks for finding the time to do this!

> Pink noise is apparently omnipresent in natural systems for kind of mysterious reasons - see eg this Quanta article, which says pink noise “is found in all kinds of electrical noise, stock market activity, biological rhythms, and even pieces of music — and no one [knows] why.”

Since pink noise = power law, the caveats expressed e.g. in https://scottbot.net/networks-demystified-3-the-power-law-rant/ apply:

> The take-home message of this rant will be that the universe counts in powers rather than linear progressions, and thus in most cases a power law is not so much surprising as it is overwhelmingly expected

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Two weeks back I had lucid dreaming experience. I knew that I was dreaming while in a dream and I felt my sense of self. The moment I woke up after that dream I was left with lots of energy to carry out the rest of the day. Now what state of consciousness was I at? In dream state. Did I lose my sense of self? No, because I was conscious of the fact that I was dreaming. Probably a dopamine surge followed up the dream. The feeling in the dream was more like decoupling from time or freeing from time such that I was able to easily float around space. What existed was the sense of self observing the dream environment. It was around 5-minute trip in wakeful state whereas what I experienced in the dream was actually lesser than that (This I am making up because I do not have a sense of time in the dream, yet it was till the end, and I was continuously processing the information. But when I compare with the events in the wakeful state, I end up processing very few information in that dream state which is opposed to what I felt because it felt as if I have been for more than an hour in the dream). That is, I was in the midst of listening to some 30-minutes Yoga Nidra script. How is this synchronization of sense of self or time related with states of consciousness? I am conscious of these events in wakeful state. Is it just an approximation I am coming up with!

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So I've seen you've taken to using AI Image generation for your cover photos

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I'd love to see what an AI set to analysing EEG recordings using Fourier analysis or whatever algorithm it decides to make up would say about them. Just as AIs embarrassingly deduce the race of CXR subjects, they might surprise us with foreseeable things (which aren't always so clear to human eyes) becoming vastly more reliable, like a diagnosis of epilepsy, but it's not beyond the bounds of possibility they could be quite good at some surprising things: ADHD, autism, Alzheimer's. We can't give them training data for that as yet, as we are not aware of EEG findings in those three examples. But if we quietly set an AI to do its thing and later supplied diagnoses as they were made, the patterns, if any, could be found and quickly refined to make a predictive diagnosis for each.

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The quote "the natural logarithm 2.17" must be a typo, because he published a paper just a few years before the book presenting the same argument but with the correct value of e https://doi.org/10.1016/S1472-9288(03)00007-4

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One of my big insights studying creative writing (and art in general) is that practically everything is vibratory / enacted by oscillation. Stories oscillate along a bunch of dimensions, and will feel "flat" if constrained. Imagine explaining the shape of a working metronome without using time: the hand is to the left and the hand is to the right. It's a paradox, until you introduce the time dimension for the two contradictory states to oscillate along. So whenever you find a paradox, it probably represents an oscillation along some dimension you haven't discovered yet. The activity of the first act of a story is to establish these paradoxes (or "tensions") which themselves establish the dimensionality of the story. They make it feel "round" instead of "flat".

A good example is Pride and Prejudice, which introduces three paradoxes (at least) in the first sentence.

It's fascinating how much this description of the brain sounds so much like one of the best ways I've found to describe an excellent novel. Like, writers talk about "resonance" between separate parts of the story, and it's considered kind of a mysterious, undefinable thing. When it happens, it feels much like how this post describes different parts of the brain tuning to each other's frequency.

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Thanks to the title of this post and book, now I have the earworm of Gloria Estefan and Miami Sound Machine:


Oh well, makes a change from the previous earworm of "Hail, Bright Cecilia"!

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Oct 21, 2022·edited Oct 21, 2022

I just want to put here this Asimov quote from 1950 'cos I think it's delicious (no spoilers, I think; it's from the Foundation "trilogy").

“The science of electroencephalography was at once new and old. It was old in the sense that the knowledge of the microcurrents generated by nerve cells of living beings belonged to that immense category of human knowledge whose origin was completely lost. It was knowledge that stretched back as far as the earliest remnants of human history—

And yet it was new, too. The fact of the existence of microcurrents slumbered through the tens of thousands of years of Galactic Empire as one of those vivid and whimsical, but quite useless, items of human knowledge. Some had attempted to form classifications of waves into waking and sleeping, calm and excited, well and ill—but even the broadest conceptions had had their hordes of vitiating exceptions.

Others had tried to show the existence of brainwave groups, analogous to the well-known blood groups, and to show that external environment was not the defining factor. These were the race-minded people who claimed that humanity could be divided into subspecies. But such a philosophy could make no headway against the overwhelming ecumenical drive involved in the fact of Galactic Empire—one political unit covering twenty million stellar systems, involving all of Man from the central world of Trantor—now a gorgeous and impossible memory of the great past—to the loneliest asteroid on the periphery. And then again, in a society given over, as that of the First Empire was, to the physical sciences and inanimate technology, there was a vague but mighty sociological push away from the study of the mind. It was less respectable because less immediately useful; and it was poorly financed since it was less profitable.

After the disintegration of the First Empire, there came the fragmentation of organized science, back, back—past even the fundamentals of atomic power into the chemical power of coal and oil. The one exception to this, of course, was the First Foundation where the spark of science, revitalized and grown more intense, was maintained and fed to flame. Yet there, too, it was the physical that ruled, and the brain, except for surgery, was neglected ground.

Hari Seldon was the first to express what afterwards came to be accepted as truth.

“Neural microcurrents,” he once said, “carry within them the spark of every varying impulse and response, conscious and unconscious. The brainwaves recorded on neatly squared paper in trembling peaks and troughs are the mirrors of the combined thought-pulses of billions of cells. Theoretically, analysis should reveal the thoughts and emotions of the subject, to the last and least. Differences should be detected that are due not only to gross physical defects, inherited or acquired, but also to shifting states of emotion, to advancing education and experience, even to something as subtle as a change in the subject’s philosophy of life.”

But even Seldon could approach no further than speculation.

And now for fifty years, the men of the First Foundation had been tearing at that incredibly vast and complicated storehouse of new knowledge. The approach, naturally, was made through new techniques—as, for example, the use of electrodes at skull sutures by a newly developed means which enabled contact to be made directly with the gray cells, without even the necessity of shaving a patch of skull. And then there was a recording device which automatically recorded the brainwave data as an overall total, and as separate functions of six independent variables.”

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So in the moments where it feels like time slows down would that mean your conscious brain rhythm is beating faster?

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Part of the difficulty with doing any kind of direct investigation of EEG tracings is that the EEG is a tremendously distorted view of what the EM fields inside the brain are actually doing. Necessarily: you're recording an electric field at the surface of a pretty conductive material. You'd be much better off using SQUIDs to detect the magnetic fields, because that would give you a relatively undistorted picture of the 3D pattern of EM fields, and I would guess the wavevector structure (i.e. the result of a Fourier analysis in space) would be at least as important as the frequency structure (the result of the Fourier analysis in time). That's expensive, though.

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> Gamma was the rhythm I found most interesting - it seems to map very well to “items in conscious awareness”. Ask someone to think about a certain topic, and cells representing that topic will form a neuronal assembly (ie they will start oscillating together at the same frequency) in gamma rhythm. I was pretty surprised that there was this clear of a neural correlate of consciousness.

I'm interested to know if anyone has taken e.g. Daniel Ingram or other expert meditatiors and tried to measure correlates to Jhana and other rarified meditative experiences. As the progression of experiences in Jhana are very clearly defined (at least in some aspects), measuring the difference in activity at each Jhana should be possible at some resolution of instrument.

Coming from another angle, when achiving single-pointedness in meditation, conscious awareness rests entirely on one object of attention, without any scattering or wavering. Presumably there's some strong Gamma there? But with extremely focused attention, are the Gamma oscillations localized in some way, relative to the object of attention that is being focused on? Would we see different activation patterns when focusing single-pointedly on different mental objects?

(The key thing here being, in most minds, attention scatters and wanders from second to second, so these sorts of localization would be impossible to do if the oscillations are on the order of Hz. But single-pointedness seems a quite unusually-stable state, which might make it amenable to objectively studying qualitative/subjective experience.)

I imagine this is something https://theeprc.org/ is into, but I don't know the meditation-neuroscience literature well.

A final take on this; in the formulation of The Mind Illuminated, we have various hinderances such as Dullness and Distraction. These should be measurable in some way; can we detect Dullness from the outside? (For example it might be the percent of Theta waves increases as Dullness is perceived? Can this help a meditator to get feedback on different states? It's said that some get fooled into thinking Dullness is a more advanced state like Jhana; can we objectively detect Dullness to help meditators avoid this confusion? Particularly Subtle Dullness, which can sneak up on you?)

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It's an intriguing idea that navigational memory underlays episodic memory. To build on your reference to the method of loci, I guess one explanation for the reason our declarative memory sucks is that we are using a system that was designed for navigation for something that has nothing to do with navigation. It makes intuitive sense: it's hard to remember abstract facts because they do not look like anything, and they are not located anywhere. I had learned that the method of loci works because humans have a *better* navigational memory system, but this is a different explanation: all memory is at root navigational, and it's not optimized for facts unless hacked by picture things in palaces.

For more on this, I recommend The Embodied Mind. It is full of gems like: people can think more clearly when they move their hands, or, people think Chile is south of British Columbia because of something to do with how we assemble objects in our heads. Movement came first, and thought, emotion, and language bare the imprint of this legacy. The book is badly written, but the examples are great.

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Apropos, a musician I play with who also happens to be a neuroscientist just published this paper on synchronous states invoked by psychedelics. He'll be presenting at the Society for Neuroscience conference in SD, CA next month. The basic gist is that there seems to be some sort of ~150Hz standing wave of activity that appears widely in overall cognitive processing centers when (a rat) is on LSD/etc.


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Oct 22, 2022·edited Oct 22, 2022

Oh boy, I love noise. From a physics, electrical engineering point of view; White noise is also called Johnson noise, it is the thermal noise inherent in all resistors*. And first reported by Johnson at Bell labs. https://en.wikipedia.org/wiki/Johnson%E2%80%93Nyquist_noise#History. (It has a flat frequency spectrum (white) at least for low frequencies... it's a classical effect. It breaks down at higher frequencies where quantum effects have to be accounted for.) Brownian noise is the noise of Brownian motion and is understood to be the integral of white noise. https://en.wikipedia.org/wiki/Brownian_noise. It has a spectrum that goes as 1/f^2 (where f is the frequency.) Both white noise and brown noise are fairly well understood from a theoretical perspective. Pink noise is called 1/f (one over f) noise in the physics community. It has a power spectrum that goes as 1/f (not surprisingly) It is not at all well understood theoretically... which is why we find it interesting.

*other random processes also give you white noise, shot noise is one other example. https://en.wikipedia.org/wiki/Shot_noise

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Speaking of machine learning and brain waves, I am surprised not to find any direct mentions of positional encoding; see e.g. «Attention is all you need» arXiv:1706.03762 where they literally say that to make relative word positions easier for the network to use, multiple dimensions of sine waves of different frequencies — independent of the real input — are added as input features.

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"If brain waves are really responsible for things like attention and cross-talk between brain regions, then might a lack of brain-wave-equivalents make AIs worse at these things"

Has anyone looked to see if there emergent oscillatory phenomena across the layers of really deep networks? Might me an interesting weekend project.

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There’s this book, Psychedelic Information Theory, which is an interesting source to fold in with this book

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"Suppose that - and again, this is a fake toy example - your unconscious is oscillating at 1 hertz and your default mode network is oscillating at 2.17 hertz. Then you meditate, you slow your default mode network to 2 hertz, all of a sudden consciousness and unconsciousness are nice multiples of each other, they reach oscillatory synchrony, and they achieve higher levels of communication with each other."

Wondering could external sound waves/rhythms with the right frequency cause something similar? Thinking about shamanic drumming causing trance states. I think I read somewhere that strongly rhythmic music with the correct frequency can cause heart-rate to entrain.

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Non-brainwave related side note:

> "[...] if you force it hard enough."

The below is a bit of an axe that I grind, but I think that even just using the word/concept "force," in relation to enlightenment will (surprisingly) cause some non-zero number of meditators to really, really hurt themselves; and I think some larger number of meditators will get sent down a rabbit hole that won't get them to where they want to go:

Scott may not mean "force" in some effortful, phenomenological, qualia-feel sense, but some people will take it that way. Sometimes the experience of "force" or "forcing" does come up in meditation. But, I believe that it's bad to "add force" to one's meditation practice.

Because, say that mind-state-space has a lot of local minima (minima bc lower is better, in this analogy). Some local minima look, at first, kinda like enlightenment (though with initially subtle and then unfolding downsides), and those local minima *can* be reached by forcing.

But, forcing sort of has "inertial momentum" ("karma"!), and many hours of forcing can take a long time to undo (thousands of hours). And, meanwhile this remaining forcing "persists," eventually running amok, causing lots of psychological, phenomenological, behavioral, and conceptual problems.

"Actual enlightenment," or whatever, is, say, at the "true global minimum" and it can only be reached by a preponderance of non-forcing and undoing latent or actualized forcing that's currently in the system.

Enlightenment state transitions can seem abrupt, and can happen spontaneously, but for most people, those "stateless states" are only going to be complete and stable after *thousands* of hours of (paradoxical) "work"--which involves tacking towards effortlessness.

(***Cryptic and prolix, understatement, but more here: https://meditationbook.page/ (Ctrl/command-f on "force" might be interesting.))

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feels like this has some super interesting implications for dissociative identity disorder that i dont understand this well enough to speculate about

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I was surprised by the discussion of AI at the end. You seemed to reach a conclusion that the fact that large regions of the brain having synchronized brain waves was either a necessary precondition of consciousness or a strong correlate of it, then moved on to say that because AI would probably not have brain waves in this sense they would be less likely to have consciousness.

My intuition is that this is backward. It seems like computationally different brain regions operating at the same frequency means either that they can communicate with each other efficiently or that they are updating on the same internal clock. But for most ML models all neurons are updating on the same internal clock and can communicate with one another with nearly perfect efficiency (i.e. little signal is being lost because a neuron is not "close to activation) It seems like AI's are more likely than human brains to have most neurons all simultaneously communicating with one another on the same internal clock.

A potential flaw in my understanding here is that in AI's neurons not firing because they are not "close to activation" sounds like a neuron that uses the activation function max(0, input). If it is currently receiving a very negative input then changing that slightly via some other neuron will not cause it to activate whereas if its input is close to zero or positive changing it slightly will change the output. Is there a proposal here somewhere that having some neurons in an ML system activate at say every 3 activations of the rest of the network and just average the inputs between activations could cause more interesting behavior or something?

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"Tune in, turn on, drop out" was a phrase evidently coined by Marshall McLuhan and certainly popularized by Timothy Leary in 1966 to encourage people to use psychedelics. If the frequency synchronization idea mentioned here is correct, the expression is remarkably prescient. Perhaps people on psychedelics literally tune in to normally inaccessible parts of their brain activity by getting them to the same frequency.

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Sometimes at 3:45 in the morning, as the party’s closing up and you are well tired, if you’re staring at a white wall you can perceive a flicker. I believe that’s alpha, not waiting for you to close your eyes for once.

If you’re not going to that kind of party much anymore, there’s also a few optical illusions that flicker around the 10hz range. The most familiar of these is the wagon-wheel effect. So just look at a car.

That’s probably as close to watching your own brain at work as it gets in everyday situations.

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Oct 24, 2022·edited Oct 24, 2022

An idea I've toyed around with is the idea of "blockchain protocols as the clock rhythm for distributed consciousness"

Like, you've got a bunch of computers running the same algorithm. Every so often, a new block gets pushed to the chain, and becomes common knowledge. This constitutes one "frame" of distributed consciousness, and contains some kind of consolidation or average of the input of all the constituent nodes.

Inside those nodes, you can have arbitrary subroutines operating in parallel wavelengths, but the outermost blockchain is like a [conductor | yearly census | drummer boy's marching beat | pulse | bible]

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I have not read the whole thing yet but I am not too surprised by the pink noise distribution? White noise is constant energy per ± X Hz, pink noise is constant energy per ± X %. It doesn't feel any more surprising to me that the noise distribution should be such that the energy between 1 and 2 Hz is the same as the energy between 100 and 200 Hz (pink) rather than 100 and 101 Hz (white).

That's not particularly explanatory but consider that the energy above refers to the "energy in the noise waveform itself", not the energy required to produce the waveform. Suppose that the energy required for a neuron to oscillate at frequency f is proportional to f, then if every neuron on average uses the same amount of energy, the output energy spectral density is going to be proportional to 1/f, i.e., pink.

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I thought we had scientific studies with brain blood-flow analysis, showing that meditation greatly curtails blood flow to a region which helps us locate ourselves (slightly above and behind our eyes). The theory was that both out of body experiences and the "one with everything" phenomenon were likely associated with this. The one time I meditated to the point of, "OMFG What just happened," I would put it more in the line of feeling everywhere than feeling at "one with everything." Maybe that was just the OMFG component though.

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Extremely vague speculation, but the golden ratio is the most irrational number, the furthest from any even ratio, and it sounds like that could make a representation of it more consistent? People always talk about it in aesthetics, maybe it's related

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I'm reading through the section on the hippocampus and how it functions in both navigation and memory. Does this connection have anything to do with how the "Memory Palace" mnemonic aid works? The one where you think of your house and assign a memory or a task to each room, and this helps you keep it all in mind later in the day.

Edit: Hey, guess what? It's mentioned in the very next sentence after where I stopped to write this comment.

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Scott: "... But as far as I know it’s the only book on brain waves ..."

As I mentioned in a comment on your "Highlights" post, I think neuroscientist William Calvin was working and writing on that some 20 years ago:


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Nov 8, 2022·edited Nov 8, 2022

Among all the oscillating brain waves, each sending and receiving signals to and from multiple recipients, it seems likely there are some neurons for which the sum of incoming signals moreorless somehow cancel each other out, eigen-neurons one might call them, for any given pattern of brainwave.

Think of waves falling on a rocky sea shore, with water swirling around the rock pools, and some will be quiescent at least for a moment, like the centres of miniature gyres. Maybe a memory is formed as a snapshot of the combination of quiescent neurons.

Someone once asked the ancient Greek sculptor Phidias how he carved such wonderfully realistic horses. "Easy", he replied "I start with a big lump of marble, and chip away everything that doesn't look like a horse". I suggest likewise that memories are formed somehow in sets of neurons whose lack of net inward, and thus any outward, signals means that they "look like" a memory, in the same sense as Phidias's comical reply!

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"If we observed closely enough, we would find that experience is actually divided into individual moments of consciousness. These conscious “mind moments” occur one at a time, in much the same way that a motion picture film is actually divided into separate frames…"

There is a passage in Ramachandran's Phantoms in the Brain where he describes a patient with some sort of brain damage that perceived the world as a series of still frames, unable to stitch them together into a continuous experience (which made crossing the street, for instance, quite hazardous).

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A couple of thoughts: (1) local cortical gamma oscillations are most commonly associated with attention. I think most neuroscientists would define consciousness as being the collection of global brain states that are different from what happens in anesthesia or damaged brains (or potentially sleep)

(2) I think that the jury is still out about whether oscillations are the signal that an invasive measurements of electrical activity detects when certain types of neurons/neural circuits are active, and all the bit about synchrony and chunking follows from the synaptic delays of the neurons involved. But I think that most neuroscientists expect this to be true. Historically folks who've studied the brain noninvasively with EEG for example imagine that the oscillations are meaningful by themselves, but that probably is just measurement bias.

(3) I've not done the experiment, but I suspect that a small antenna placed in or above a densely multicore processor would detect "local field potential oscillations" associated with different pieces of code running, and with enough resolution, associated with particular chip regions. Konrad Kording has a paper on this idea whose conclusions I don't love, but which is interesting in concept. From that perspective, there's nothing special about "wetware" except that the frequencies are a bit lower. Modern neuroscience has revealed that the old idea that a neuron was like a transistor was quite underestimating complexity. A neuron is probably more like a professor core. So it's not unlikely that some sort of machine learning/AI running on millions of cores would exhibit signals much like LFP...

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>All of this hints at some deep connection between brain waves, consciousness, and selfhood.

Neuroscience is data rich, but theory poor. You often hear this lament from neuroscientists. What's even more unfortunate is that we actually do have a wonderful, mechanistically precise theory that explains how a specific type of brain waves give rise to consciousness (and selfhood).

Stephen Grossberg, a tragically under-appreciated pioneer in computational neuroscience, has over sixty five years put together a magnificent framework that explains how a limited set of constraints and computational principles can explain consciousness and why it is needed. His work is largely unknown even within neuroscience because he was very early (early 1960s) in realizing that the complex nonlinear dynamics of brains needed a different type of math to capture rhythmic activity (differential equations). He was doing theoretical neuroscience before the term neuroscience entered official usage. Later advances in techniques (fMRI being the worst offender) meant that it was easy to publish research by generating more data than making sense of it using a good theoretical framework.

Coming back to why we need brain waves for consciousness. Here’s a very short and lossy summary (more in the links below) . Please note that the theory itself is mathematical, mechanistically precise, and explains a ton of neuroscientific data. The following is an attempt to translate it into accessible plainspeak that avoids the “the grundicular region modulates output from the parashmoxular cortex” thicket you mention.

We cannot ask what consciousness is, without also asking *who* is experiencing this consciousness. That is, where does the self that experience come from? How do 86 billion neurons (37 trillion cells in the body, really) give rise to selfhood and is consciousness necessary for this? So to explain consciousness, we must explain the self, and to explain the self we must explain, what? Is there an infinite regress?

Grossberg’s Adaptive Resonance Theory helps us understand how to break this up by recasting one in terms of the other.

The Self is simply a constellation of experiences.

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

We now merely have to explain how an experience is constructed. Here’s where the brain waves come in. We are actually incomprehensibly large beings with billions of neurons and trillions of cells with data in some large multiples of those numbers streaming in incessantly. How do we survive this onslaught and make sense of it? Meaning must be collectively manufactured by a very large number of these neurons coming together. Unfortunately, communication is expensive and long-range communication is even more expensive and we have a finite number of calories that we can use up. Synchronizing brain waves – synchrony across any oscillators for that matter - are the most energy efficient way to do large scale communication. This is also why synchrony appears so often in dynamic systems (stadium waves, fireflies, etc). They reorganize to take advantage of this energy efficiency. These brain waves help experiences in different parts of the network (brain) talk to each other if necessary to decide what to do with the sensory data that is streaming in. Note that this is merely data. It only becomes information after this cross-talk within the brain across experiences. This is what is often referred to as top-down expectations. Top-down expectations (experiences) talk to bottom-up inputs (sensory data) and arrive at an agreement through synchronizing brain waves. This is what we consciously experience as experience. Here’s the magical part. Once forged, this new experience becomes a part of the constellation of experiences we recognize as the self, changing us. No man crosses the same river twice. The constellation of experiences changes, changing the next experience.

I expand on this here: https://saigaddam.medium.com/understanding-consciousness-is-more-important-than-ever-7af945da2f0e

I would strongly encourage anyone reading this to attempt reading Grossberg’s article [1] (where he puts it all together) or his book [2], which can be a little daunting.

[1] Towards solving the hard problem of consciousness: The varieties of brain resonances and the conscious experiences that they support


[2] Conscious Mind, Resonant Brain: How Each Brain Makes a Mind


And if you are really averse to wading through grundicular regions and parashmoxular cortices, you can start with our book [3], where we offer a gentle introduction to all this.

[3] Journey of the Mind: How Thinking Emerged from Chaos


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That was a great review! You really went to the heart of the matter. Some of your questions are also brought up in this recent article on “Resonance as a Design Strategy for AI and Social Robots.” If you are interested in the brain as an oscillating system, you may appreciate the effort we took to clarify key concepts.


And, “Harmony in Design: A Synthesis of Literature from Classical Philosophy, the Sciences, Economics, and Design” has a section on brainwaves. It reviews the evidence for, e.g., brain waves being structured like octaves. Which they weirdly are—so as to support phase amplitude coupling.


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What do you call the way of thinking that looks for 'purpose' of phenomena, as if emergent phenomena were a mechaism put there as a tool towards some goal? I feel like this approach is like looking at the waves to study fish.

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This makes me think about mechanisms of ECT. What if it’s less of a blah blah BDNF story and truly a matter of brain rebooting itself after the post ictal suppression on EEG?

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Can confirm: synchronization of brainwaves seems to be what's going on in meditation - well, at least once I had that experience.

I didn't really know anything about brainwaves or -rhythms, but I back then described the experience in terms of things being out-of-phase and slowly getting in sync. More precisely, I got into the rare (for me) state when my attention wouldn't drift from the sensory meditation object virtually at all. Nevertheless, I noticed that there seemed to be small, sub-second gaps between noticing/experiencing the sense data, a sort of frame rate of stimuli. I kept my attention on that "rhythm" of attention and the gaps between, of ~ 5 Hz (that's the number I guesstimated, but hard to really tell) while it slowly was getting faster. Finally it locked into phase with my attention. The result was weird and pleasurable "liquid" compared to "granular" sensation of all stimuli, like I could take in the sense data at the rate it was coming in. Keeping with the frame rate analogy; if you can only render every nth frame, things look and feel janky, while now every frame was rendered.

It's been some years since that experience and I hadn't had it since (among other things, I'm sure I kept trying too hard to repeat it). That time it lasted some hours without effort, I guess just by its "momentum". My instructor's response to my description didn't really help me understand this and I felt I was seeing and describing things from such a different angle that no knowledge could be shared. This review got me interested again in meditation as it seems to give some framework that seems to match my experience. From here, it's possible for me to derive an explanation of a certain kind of meditation that is so simple and concise it can be expressed in a sentence without being too proverbial or abstract:

syncing your attention's frequency with the frequency (or it's multiple) of the object of your attention.

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