High definition holographic memories of every experience in our lives (every cognition) are trapped in our minds, most humans simply cannot access them.
Ordinary minds rely upon “referential” memories, and every time the mind cognates over a memory, it makes a new referential memory of that cognition.
Aging brains have more and more referential memories, as well as general atrophy, loosing coherence with their original.
There are exercises which one can do which will develop and enhance the exploration of holographic memory, without neurotic compulsive modifications (it’s a bad habit). The mind is an advanced technology we use poorly.
Ignore my account if you wish. The handling of memories as the corruption through conflation and obfuscation of the original is widely accountable.
The part of the holographic memory is unexplored for obvious reasons (beyond modern scientific reach.) Many individuals extraordinary behaviors may be explained by the lack of neurological access by others, not information storage availability.
Compaction is not even an intrinsic function of an LLM, but of it's harness or whatever code is calling it. When an LLM compacts something it's just creating a summary of the previous conversation using the same mechanism that it uses to generate any other text. It doesn't "forget" the text from before the summary, it just no longer receives it when it is invoked.
In contrast, when we forget something we are not summarizing anything, we are just losing the ability to recall something. We might remember what we have forgotten later on, showing that it is still somewhere in the mind, and that the reason we have forgotten it is not necessarily because it has been discarded.
Even then, what this article is talking about is not even normal forgetting as a function of the brain. It's talking about a degenerative and dysfunctional form of memory loss where different memories are confused with each other.
This comes as absolutely no surprise to me -- my mother was recently transferred into memory care, and the changes to her memories are definitely showing some interesting changes. The foremost was that her new "apartment" is shared with one other person, gives off a bit of a dorm room vibe. First day, she was talking about how excited she was to be going back to school and seeing her boyfriends again(?!). I said, "Oh, like Doug? [my dad]". Her response? "Ew. No."
But, she's come around on that, and recently been upset about the affair he's been having (they divorced in 1980), and is irate over him having another kid with his new wife (1983), but isn't surprised that I haven't spoken to him in 12 years. She's never surprised at how I've aged, but doesn't remember her grandkids even exist and insists on meeting them soon (but remembers them when she sees them). Two weeks ago, she called to yell at me for things I did in high school (1990). At this point, I seem to be the anchor for everything recent, but when I'm not around physically, it's scattered everywhere else.
Thank you. Surprisingly, less than you'd think. She started showing the first signs around 2020 or 2021, so by the time she had a diagnosis in 2023, I had pretty well wrapped my head around it. (Having a regular therapist has helped a bunch, too.). It's still tough, but that's a difficulty that's spread out over years rather than a sudden out-of-nowhere illness.
It's hard to write this, but before my grandmother died about 45 years ago, it felt like she'd lost almost all her memories, but was easy to take care of. She was in her late 80's.
My mom & aunt took yearly turns keeping her in their homes. If you kept her away from the stove, she still seemed quite happy helping fix her own breakfast, taking her meds, etc. Give her a warm washrag after, and she'd wipe down the table and then proceed to putter all around the kitchen, wiping and straightening. She preferred cartoons on TV (easier to see, I guess), and was always glad to fold clean towels or clothes, if she had the chance.
Once, she actually recognized me, and remembered my name. It was a very precious 5 minute chat, but then the memories were gone. Still, I'll always remember her that way, because it was just like how I remembered her, when I was a child.
Man, that makes me happy. I'm guessing your grandmother was a very upbeat, happy person who dealt with change well. My mother's the opposite, so now every little thing going wrong is the end of the world. It's rough, but I also get to see these moments where I can see how she was when I was little/before I was born, and those are moments that I do really enjoy.
These stories scare me. I'm happy that you were able to enjoy glimpses of your mother's past.
Mine is showing signs that several of us believe to be dementia, but she refuses to be seen and screened. It has gotten worse over time - repeated conversations, sometimes inside the same phone call, and a shift from "can't remember things if she doesn't write them down" to "needs to ask on multiple occasions because she hasn't remembered to write it down" - but not to the point where she seems to be a danger to herself.
I love my mother, but she is difficult. She also has grown with age. While she is difficult now, she used to be abusive and manipulative. My father would never speak ill of her, but from some things she's said I believe she was worse before she was raising us. If my mother regresses to an earlier state of mind, we will both need professional help. She will need professional care, and I will need help to not internalize the way she treats me.
The paper certainly has its limitations. Only 61 participants, with almost nobody between 30 and 50, so we shouldn’t read the age trend as a decline across lifespan. What’s more interesting than the title suggests (and I find the title forcing the conclusion a bit) is that the attention measures were not linked to age or the brain patterns at all.
Then again, i'm 45 and I've been losing my keys and my IDs since I was 20
The entire article is AI-generated.I wish such sources would include the prompting used as well as the model so a (human) reader can better evaluate the document.
> This process includes multiple checks using different LLM platforms, human editorial verification, and cross-referencing with original sources to ensure accuracy and eliminate false information, misleading content, fabricated content, bias, and unreasonable speculations or editorialization
A less traveled road to a cool story would be that, while dreaming, you are experiencing unfiltered realities from many nearby timelines simultaneously (hence the fluidity of the reality of dreams, where things change from a moment to the next).
I recently had a very curious experience - my in-dream me recalled a memory from a past dream. It was a really strange feeling that definitely added to the "realism" of the dream.
I wonder how much of this is directly related to age, and how much is just someone's brain becoming "full" due to more memories getting added every year?
It seems that memories must be stored as embeddings with single multi-neuron assemblies (cortical columns?) storing multiple embeddings as a kind of contents-addressable memory that is able to keep memories distinct due to the very high dimensional space (# neurons per assembly) being used. However, you'd expect that at some point if you store too many memories in a single assembly the recall accuracy is going to go down.
You'd expect that with big brains being so costly, evolution has only equipped us with brains big enough to store a lifetime of memories, so it would be odd if memory didn't suffer as we get old.
To make a computer analogy, it's a bit like a hash table getting too full. Say you had a hash table without any overflow mechanism... up to a point recall may still be pretty good, but as the table gets closer to full there will be more hash collisions and likely hood of "false recall". Obviously the brain is not a computer, but the analogy may hold up reasonably well if you consider the hash table keys and values as embeddings and the store operation being an embedding merge rather than overwrite.
The brain does not have the Von Neumann bottleneck. Unlike most current digital systems, the brain doesn’t have a separate memory registry it needs to pull from.
Engrams, that is, the physical trace of a memory, are not stable through life. They start out in the hippocampus, but as the stimulus recedes in time without reinforcement, it moves away.
No evidence exists though that the memory is encoded in one set of cells. This spatial segregation of memory is the worst hangover from the “brain is a computer” analogy. Even if it is, why in the world would it be like our digital devices which specifically have the Von Neumann bottleneck? In biology, memory and processing are not segregated.
There’s growing evidence the memory is much more distributed over the network, and is recomposed based on salience overlap with a new stimulus.
Another factor to keep in mind is circadian rhythms. There’s growing evidence for how much the memory system and timekeeping system overlap, at a molecular level. Every neuron (and other cell) has an intrinsic clock that ticks at roughly 24 hours, and continues to do so even in total darkness.
When you encode the memory has a lot to say, based on your chronotype, on how and how well you will remember it. Same with learning: there’s a time of day based variation.
Sleep, and dreaming, is when these memories seem to get replayed and critical features and connections are incorporated into the system and its regime, awaiting the right triggers to access a state similar to when the memory formed.
I’m stitching across a lot of different research, and I want to be clear many aspects of this system are not yet fully worked out.
But what we do know points to a system that works with different physical and algorithmic priors, and the dynamics are sharply distinct from current digital computers.
This is really fascinating. The actual mechanisms of the human mind are distinct from computer systems, yet there are some parallels.
There are some hints that increased memory access times scale with the amount of information the brain has stored vs the more typical narrative that aging decreases the capabilities of the brain.
> Our results indicate that older adults'; performance on cognitive tests reflects the predictable consequences of learning on information-processing, and not cognitive decline. We consider the implications of this for our scientific and cultural understanding of aging.
> The brain does not have the Von Neumann bottleneck
Obviously not, which is why I didn't say it did!
However, if you want to identify where long-term memories are stored, then that is in the cortex, but it should go without saying that this doesn't make the cortex the storage component of a von-Neumann architecture!
> No evidence exists though that the memory is encoded in one set of cells
I'm not sure what you are trying to say.
Memories are presumably stored as embeddings - a distributed representation, and an episodic memory may well be stored as "chained together" episodic "scenes/chunks" where each chunk recalls the next.
However, a distributed representation isn't the same as a holographic one, and any redundancy may well still be localized within given cortical columns, so I think you may be wrong if you are saying that individual memories/chunks are not confined to one set of cells (some localized neural assembly such as a cortical column).
> Obviously not, which is why I didn't say it did!
But you sneak it into your assumptions on what a memory must be.
> However, if you want to identify where long-term memories are stored
And why do you assume there is a specific “where” for the memory?
> then that is in the cortex
There’s good deal of evidence disproving this. The cortex is involved in sensing, and yes, the sensory information associated with a memory will recruit appropriate cortical cells. This doesn’t mean the memory resides in the cortex.
> I'm not sure what you are trying to say.
Let me restate what I’m saying then:
There are four claims bundled together in the way you were describing biological memory: that a specific ensemble is activated when a given memory forms; that it’s the same ensemble that gets activated over time when that memory is retrieved; that it’s spatially compact, like a column in region of the brain; and that this ensemble it’s dedicated to that memory, or similar memories .
The first is well supported. An engram, a network of neurons, is indeed activated when a memory first forms, and gets stabilized due to repeated stimulus. Re-activating these neurons in a different context can make the subject (a mouse) behave as it would if it had contextual signals to evoke said memory.
However:
1. This engram is not in one particular part of the brain. There’s a cortical part that overlaps the sensory regions that were involved. But plenty of other regions are part of the engram
2. There’s turnover, over the course of weeks, when the specific cells involved in the engram drift, while the behavior remains stable.
3. The same synapses participate in many memories.
> Memories are presumably stored as embeddings
No. Let’s consider songbirds, which are an excellent worked out example (in an animal with no cortical columns at all, by the way).
What’s learned is a temporal sequence, with neurons in a nucleus in their brains each firing one brief burst at a fixed point in the motif, so the content of the memory is its dynamics rather than any value. The circuit that evaluates the match against the tutor template is the same circuit generating the output being evaluated. Song degrades overnight during sleep replay and recovers the next day, and in seasonal species the song nuclei change size across the year with neurons added and lost while the song persists. There’s no read that leaves the item untouched, no persistent address, and no substrate holding still. “Stored as” imports all three.
And it goes below the neuron or synapse. Hearing a tutor song drives immediate early gene expression that habituates with familiarity, and singing drives large transcriptional changes in the song nuclei that differ by social context for the same motor output. Since transcription runs on minutes to hours and the proteins turn over, any persistent state has to be actively regenerated rather than deposited.
TLDR: the memory isn’t a static store. There’s no “location” for it, distributed or otherwise, though specific locations can be in the chain that’s activated for retrieval/productuin. Instead, memory, over time, is driven by a dynamical regime that adjusts its dynamics to account for the temporal pattern in the salient stimulus.
Nothing, down to the epigenetic changes in the DNA of these neurons, can be seen as “the” location of “a” memory, especially over time.
> if you are saying that individual memories/chunks are not confined to one set of cells (some localized neural assembly such as a cortical column).
If someone's hippocampus is destroyed, they lose the ability to form new (episodic) memories, and may lose some more recent old ones, but they certainly do not lose older ones. This is basic knowledge.
> and that this ensemble it’s dedicated to that memory, or similar memories
No - that's the exact opposite of what I said. My whole point was that a cortical column is NOT dedicated to a single memory (we'd run out of memory!), but rather acts as an embedding space containing many (sparse) embeddings.
Due to the size of the embedding space and sparsity of individual embeddings, there is little chance of much overlap between embeddings and therefore associative recall is reliable. When there are too many memories stored using the same set of neurons, then there will be non-trivial overlap between embeddings (this is the definition of "too many" / "full") and associative recall becomes unreliable.
Note incidentally that this explanation holds regardless of whether distributed embeddings are stored in a more localized area (or areas - visual, auditory, etc components) or more globally distributed. At the end of the day evolution has equipped us with a right-sized brain, and an individual that outlives the useful life it is adapted for can expect to experience memory failures.
I'm not sure why you bring up bird brains, and specifically bird songs(!), but FWIW it seems that their short term memory likely works similarly to our own in as much as it is based on the hippocampus, with a very strong correlation between bird hippocampus and memory capacity (ability to memorize 10's of thousands of hidden seed locations in some species). Some birds such as crows certainly have long term memory where I'd guess those may have migrated to their pallium, but we're discussing human memory here (or at least I thought we were).
> If someone's hippocampus is destroyed, they lose the ability to form new (episodic) memories, and may lose some more recent old ones, but they certainly do not lose older ones. This is basic knowledge.
Yes, like basic reading without digging into details. You must have heard about HM, since you’re saying all this. But here’s the facts:
When H.M.’s remote memories were probed carefully, they turned out to be gist-like and semanticized, not vivid re-experiencings of specific events. Here’s the paper:
Only semantic memory of the episodes can be said to be “cortical” (though please note, lack of hippocampus doesn’t mean lack of other brain regions…). Rich recall absolutely does require the hippocampus.
Once again, please try not to “spherical cow” the complexity of the brain to try and fit it into your analogy to digital computing. You will get an underdermined model that will miss the subtleties, and lead you to claims that are poor fits for the reality.
As for why I brought up bird brains… there part of the same evolutionary web. Is there some reason you want them excluded? They’re a well studied model for a fairly complex memory task, using substantially smaller neurons more densely packed in a different architecture than mammals.
In cognitive science, as in computer science I’d imagine, it’s useful to look at the full picture before making strong claims.
The bird case is interesting because the region of interest is a nucleus, rather than cortical columns, and actually has well documented structural variations in size, as well as gene expression, over the seasons, while the memories are forming.
If your model is correct, it needs to account for those facts.
> If your model is correct, it needs to account for those facts.
Let me make it simple for you.
We have a finite number of neurons in our brain, as do birds, and our brain is attempting to store an ever growing number of memories in those. And, no, this is not a digital computer (is your reading comprehension really so bad?).
Nobody, including you, knows exactly where different types of memory are stored, and for my argument it makes no difference. What does make a difference is how they are represented, which I am suggesting is sparse embeddings.
So far, you've been ignoring my actual argument and instead responding to various strawmen of your own making, so it's not clear if you even understand what a sparse embedding is.
If you do understand, then it should be obvious that it makes no difference whether the neurons comprising this embedding space are in the hippocampus, cortex/pallium or anywhere else. Clearly you do NOT understand, since you bring up bird brains (pallium vs cortex) and want to argue about location of storage (hippocampus vs elsewhere) as if it made a difference to MY argument.
My argument (if you care to respond to it, which so far you have not) is that when sparse embeddings have little to no overlap, then associative recall by a similar pattern will work reliably, but when multiple embeddings have too much in common then recall will suffer as multiple embeddings will match.
Hint: if you think this has anything to do with digital computers then you have misunderstood and need to go back and re-read more carefully, or google for any terms you do not understand.
Sliding past the mistakes pointed out, shifting goalposts and trying to recover I see.
Let’s say I’m a complete moron and don’t know what a sparse embedding is.
Pretty please, can you define it for me and then tell me, in detail, where in whatever region of the brain you think this is going on… how is it going on?
Explain how “memories must be stored as embeddings with single multi-neuron assemblies (cortical columns?) storing multiple embeddings as a kind of contents-addressable memory”
You have moved past the cortical column. But still seem to be insisting it’s a bunch of neurons, somewhere… or has that also conveniently changed? Whatever your current position is, please go ahead and explain what components of what cells or otherwise are involved in this process you’re describing.
An embedding space is a (typically) high dimensional space that has enough dimensions such that examples of some type of entity (e.g. faces, words, or thoughts) can be represented as points in that space, positioned such that they are nearby to other entities with which they have things in common.
An entity embedding doesn't need to use all the dimensions of the space it is positioned in - some dimensions may be unused (sometimes implemented as a coorrdinate of 0 in that dimension). These are called "sparse" embeddings. For example, an LLM's tokens are represented as embeddings in what is typcially an approximately ~1000 dimensional space, but start out as sparse embeddings just representing a short letter sequence (but then go on to be transformed/augmented with additional information and so become less sparse).
As an example, let's say an embedding space has 10 dimensions, then a couple of sparse embedding examples could be:
[0 0 1 0 0 1 1 0 0 0]
[1 1 0 0 0 0 0 1 0 0]
These two embeddings have no overlap (where both are non-zero), and the more dimensions you have the more likely it is that two random sparse embedding will have little in common.
Embeddings are used in many types of artificial neural networks, not just LLMs, for example face recognition networks, where they are trained such that similar faces (multiple photos of the same person) are close together in the embedding space, and post-training you can then "look up" any arbitrary photo (in the training set or not) by embedding it and seeing what is nearby in the embedding space, which will be similar looking faces.
Presumably real neural networks used embeddings in a similar way, since, for example, it obviously requires many neurons to represent the many differences between different faces, and there is going to be overlap between the neurons used to represent multiple faces (this is not a computer with one storage location for face #1, and a different location for face #2).
A neural network, real or artificial, uses groups of neurons (e.g. a cortical column) to represent an embedding space, with each neuron corresponding to a dimension. A single group of neurons (column) can store multiple embeddings (e.g. faces) represented as different activity patterns (which neurons are firing), and if these are sparse embeddings then the firing patterns corresponding to different memories stored in the same column will have little in common.
Now, I don't know how you believe associative recall is implemented in the brain - how does someone's voice, or half obscured face, recall their entire face, so feel free to imagine it as implemented however you will, but I'd suggest that in an assembly such as a cortical column that when a set of synaptic inputs are triggered the assembly as a whole will learn to reactivate the entire pattern when only part of the original set of synaptic inputs are triggered, and this is the basis of associative recall. There are papers that suggest exactly how this may work given the cortical column microcircuit.
So, with all that said, the suggestion I was making for why (or at least one reason why) memory degrades with age, with memories blending together, is that with a finite quantity of "storage" (cortical columns) you will eventually be storing so many memories (absent a deliberate forgetting mechanism) that there will inevitably be overlap between the sparse embedddings, and this associative recall will therefore not cleanly recall individual memories but rather recall blended memories according to what they have in common.
Obviously some types of memory are at least initially stored in the hippocampus, so no reason to focus on cortical columns, but I expect the use of embeddings is universal.
Ok great, thanks. Now you’ve brought up facial recognition, and that’s actually a great example to show where the analogy breaks, and the idea of a few sparse cells encoding specific faces has been conclusively disproved:
Faces live in a ~50-dimensional continuous space (25 shape axes, 25 appearance axes). They measured about 205 neurons across 2 macaques (human studies have substantiated much of this, some from the same lab), and the key thing is: every neuron participates in every face.
The paper shows faces are embedded, but as points in a dense linear space where neurons are axes, not as sparse activity patterns where neurons are on/off slots.
The mapping between the neuronal activity and the facial structures is invertible. Record these same cells, and their firing pattern can be used to reconstruct the face. Or, if you generate a novel face, you can predict the firing rates of these neurons for it. As far as I understand, this doesn’t work for sparse embeddings.
Some cells carry the shape coordinates and others carry the appearance coordinates, in a heirarchy.
There’s an embedding space, yes. But that space isn’t defined by a network of “on” and “off” neurons. The embedding space is instead constructed by the activity of neurons, and the differences in activity distinguish the faces, using the same set of neurons.
And distance in the ensemble activity of these neurons tracks the distance in face space.
If faces use sparse embeddings, you wouldn’t expect similar faces to evoke similar activity would you? Yet that is exactly what this paper shows, and the same has been shown in the human brain for faces.
There are places where it’s sparse activity of a subset of neurons that maps to specific memories. What you’re describing is what you’d see if you look at how the dentate gyrus (part of the hippocampus) handles your memories in the same location.
But even there, the sheer number of cells makes this combinatorially such a vastly overdetermined system for a lifetime that there’s no capacity limit of the kind you’re describing. Even 1% of these cells lighting up for a specific memory leaves you with so many possible combinations that you’d have to live for a few million years to be in the right scale to at least being to talk about capacity issues.
The brain just isn’t capacity limited by the number of neurons the way your intuition is pointing you.
If you say this has nothing to do with the Von Neumann bottleneck or computational functionalism, fine, but how do you square that with the statement below, which you made further down responding to another post?
> but it's hard to imagine that all of the classical chemistry, let alone quantum, details are important. It's necessarily built out of chemistry, but selection is happening at the level of behavior - presumably depending only on a much higher level set of abstract capabilities (ability to learn, etc), not the exact details of chemistry.
The success of LLMs, a crude prediction mechanism built atop a crude ANN, does tend to support the idea that low level details don't matter. Timing will matter if we want to go beyond LLMs to AI that can learn time-based things and not just sequence order, but how much else will matter remains to be seen!
It’s really odd to see these two paragraphs, because the second actually tells you why your first is wrong.
Simply put, the biochemistry is timed. I urge you to study how temperature compensation of circadian rhythms is achieved. That anticipatory function goes all the way down to the molecular level.
It might go down to the quantum level too. In birds, magnetoception depends on a protein called cryptochrome IV, which uses a singlet born, entangled radical pair of electrons to sense the very weak magnetic field of earth.
> Now you’ve brought up facial recognition, and that’s actually a great example to show where the analogy breaks, and the idea of a few sparse cells encoding specific faces has been conclusively disproved
Well, my analogy was relating computer hash tables collisions to sparse embedding collisions, so what you are discussing now is my suggestion itself, not the analogy, which is fine!
The study we're discussing was nominally about associative recall, not faces per-se, and specifically about the hippocampus not the cortex (that Macaque face study).
> Memory accuracy for pairing faces with objects and scenes dropped sharply
That said, I wouldn't be so sure that face embeddings are fully dense, even if they are not particularly sparse either, given that not all faces have the same set of features, such as facial hair, glasses, blemishes, etc. OTOH, it's possible, perhaps likely, that similar faces are stored together, in which case they may be more dense.
> If faces use sparse embeddings, you wouldn’t expect similar faces to evoke similar activity would you? Yet that is exactly what this paper shows, and the same has been shown in the human brain for faces.
With embeddings in general, sparse or not, you'd expect individual dimensions/neurons to represent different axis of variability, so you would expect individual neurons to be active for multiple different faces that are similar along that same axis. Note that the study you are citing used individual neuron recordings as well as fMRI, but of course we don't currently have the ability to simultaneously record from the hundreds of neurons that are likely being used to embed faces, so I don't think this study has much to say about the degree of sparsity of these embeddings. Obviously IF faces both with and without glasses are stored in the same embedding space (same set of neurons), then one would expect the "glasses neuron" not to be firing for a face without glasses, which would confirm some degree of sparsity.
> The brain just isn’t capacity limited by the number of neurons the way your intuition is pointing you.
It's highly unlikely that our brains are wasteful and have unused capacity - this recalls daft pop-sci articles saying that we only use 10% of our brain ... We know that brains and memory do degrade with age, and the only question is how - maybe the encoding mechanism itself is failing resulting in embeddings that have more overlap than they should (or one could hypothesize a dozen other possble failure modes). Do you have any theory that explains the "aging brains blend memories" study that we're discussing, at the level of detail of the hippocampal patterns they are seeing?
> It’s really odd to see these two paragraphs, because the second actually tells you why your first is wrong.
> Try as you might, you can’t separate out the deep linkages from the molecular to the behavioral in biology.
Of course the linkages are there since our brain is built from chemistry, yet selection pressure is happening at a much higher functional level. The part of my response you are referring to is addressing the question of how much of this molecular level detail needs to be retained in an ARTIFICIAL neuron model sufficient for it support the same phenotype-level functional behavior, and the answer is we just don't know, because nobody has yet tried to do it.
Prior to LLMs a lot of speculation about what is necessary in the brain to learn language, e.g. Chompysky-ian language-organ nonsense, might have sounded logical and compelling, but now we have proof-by-existence that "prediction is all you need". We're going to need to wait until we have built an artificial brain, capable of learning time-based phenomen...
> A neural network, real or artificial, uses groups of neurons (e.g. a cortical column) to represent an embedding space, with each neuron corresponding to a dimension.
You:
> The paper shows faces are embedded, but as points in a dense linear space where neurons are axes, not as sparse activity patterns where neurons are on/off slots.
So you are saying that neurons are axes (aka dimensions), exactly as I just said!
> If faces use sparse embeddings, you wouldn’t expect similar faces to evoke similar activity would you?
Yes, of course you would, because that is precisely how embeddings work, and how you recognize someone even though their head is turned or they are wearing a baseball cap or whatever.
This is the ENTIRE point of emebeddings and why evolution has discovered them as a way of representing things and a way to recall them. You may have seen someone a million times, and yet the sensory patterns your visual cortex is fed are likely different every single time because they are not in the exact same orientation, making the exact same facial expression, with the exact same haircut, etc, etc, etc.
To your brain these are merely similar inputs, similar faces, but there is only so much facial variation between individuals, and if the input is similar along dozens or hundreds of axes of variability (i.e. close in embeddign space) then it is alomst certainly the same individual.
Note that "recall keys" (embeddings) are typically sparse even any stored embedding is not, since the face you are looking at may indeed be turned left or half obscured, and this partial/sparse pattern needs to recall the full one.
How can you be a neuroscientist, or even self-identify as one, if you are not already familiar with things like embeddings, and are making such basic 100% wrong assumptions as "you wouldn’t expect similar faces to evoke similar activity" ?!!!
There is 2006 paper "Polychronization: Computation with Spikes" E. Izhikevich. that describes one simulation they have done in silico and it explains how exactly distribution of activity happens and why you can't run out of memory. Basically the small group of neurons, say 10, can represent much larger amount of information say 1000 because they can fire in different orders, that is what they call poly-synchronous activity.
My theory was that memory had some form of static store, something molecular like methylation. I hadn't considered something dynamic/temporal like you describe.
Would delay line memory be a fair analogy? Information stored in a delay line memory never stays in one place. I think you are saying biological information is "stored" in the amplitude and phase of oscillations of our cells. Is that correct?
I'm still inclined to believe there is some form of static store, which would be required for inherited memories. Things like our (and many other mammals) ability to recognize emotions in others. Somehow our gametes encode what a happy, sad, or scared face looks like. I instincts as inherited memories.
None of that changes whether there is a physical capacity, which I think was the larger point? There is no reason to believe distributed memory doesn't suffer from the capacity component of the bottleneck. Btw I would advise against the absolute statement that there's absolute segregation of memory and processing.
I guess my point is the brain not being "von Neumann" in architecture or digital is not proof that isn't a "computer" of some sort.
> None of that changes whether there is a physical capacity, which I think was the larger point?
Capacity in what sense? Are we saying it’s X MB of data the brain can store? That claim is steeped in assumptions.
On the other hand, no one is claiming the brain has infinite memory or anything. And it’s certainly not a very accurate memory system. I’m arguing against “capacity” being understood as “these specific physical components located here and here we can ID store memories, and can get crowded with too many memories” sense.
This most particularly fails because not all memory is even identical in the brain, whether we mean the physical changes associated, the topology of the information, or how it’s activated.
> There is no reason to believe distributed memory doesn't suffer from the capacity component of the bottleneck.
I didn’t know there was a capacity component to the bottleneck, only a bandwidth one.
All I’m trying to say is that analogy to current typical memory storage systems to explain the brains memory processes is not helpful.
> I guess my point is the brain not being "von Neumann" in architecture or digital is not proof that isn't a "computer" of some sort.
Indeed, since the word computer was first used for humans. But what kind of computer matters enormously. Ising machine? Quantum+classical stack? Reservoir computer? All those frameworks have processes in the brain they can point to as homology.
Which points to a possibility: maybe the brain is multiple types of computers interacting. And the physical realization of these computing architectures aren’t spatially separated but thread through each other in the biochemistry and physical dynamics of cells.
Perhaps undiscussed in the paper is how the blending of memories may also be better. If evolutionary pressure encourages the structure of brain to optimise for accurate prediction, not memory, then it is quite possible that older individuals may be making better predictions than younger ones, in situations where they don't remember the details...
I'm not sure about better, unless this is the normal mechanism for generalization, but if a form of generalization (not just confusion) is the effect, then it'd be a nice form of graceful degradation, whether selected for or not.
I wonder to what extent evolution selects for longevity (& graceful degradation) past a certain point? I would think that once you are past breeding age it's generally more beneficial for the species if you die off and make way for the next generation, other than having some residual benefit as babysitters for the grandkids and leading the herd to the water source in the next drought.
My understanding is that its just straight up deterioration? A wishful view might be something like a pressure toward generalization(wisdom?) allowing for partial overwriting/grouping of specifics. Like a coarser but more predictive representation.
Hijacking your post with a dubious segue because I’m itching to bounce these thoughts off somebody:
I’ve been consuming a lot of talks / writing recently about “enactive” pictures of how our brains function. From what I gather, recent studies have called into question the entire idea of real world concepts being “represented” by an area of the brain at all. While it’s true that atandard fMRI-style snapshots of brain activity are semi-stable over the course of a short experiment, it’s not true over longer timeframes. The response to the same stimulus will change over time. They refer to this as “representational drift” in the literature, and some people are using this to bolster theories of mind that they consider non-representational. They instead emphasize the brain as a kind of dynamical system that learns to “resonate” with the world to pull itself back into homeostasis. The focus shifts away from facts and memories as data, and sees neuronal plasticity more as a mechanism for tuning the brain’s resonant frequencies. This obviously places high importance on the spiking, recurrent nature of actual neurons, as opposed to the neurons-as-functions / back-propagation / ML approach.
My mind’s not made up on how interesting and revolutionary this approach is / isn’t. The distinction seems to be about whether learning is more like “writing to disk” or “tuning a PID controller” - but in either case, the world is leaving a stateful imprint on your brain that will impact how it processes future data. Is that important to understanding how brains work, or is it just semantics?
Some years ago, I heard a retired Lutheran priest ponder, in radio, about the concept of a prayer, and whether, as an edge case, an unborn child could be able to pray, with the child of that age having no understanding on the required concepts. This is apparently one of those questions that people ponder under the umbrella of philosophy of religion. His conclusion was that prayer was about harmonizing one's self with the universe. It's a beautiful thought, (and I think religions, of all kinds, are really good at providing the fertile ground for thoughts like these).
It's akin to how the saying goes, that when we argue, we need to reach the same wavelength where the other one is to reach an understanding.
In communication studies or sociology or linguistics or one of those fields, there's the idea that communication is about making pacts about meanings, and finding the common ground to understand and delimit the message.
In a sense, from that basing, one could argue that understanding can be seen as an act of harmonizing. I think there's something universal in it.
For a lot of people, I think religion provides this basis or promise of harmony with the world, in a simplified way that stipulates what you should or shouldn't do and how things work, and those people find comfort in having these clear "rules"; we want to be able to rely on our understanding of the world, so that we can act within it with confidence, or faith. Ultimately, the "god" that is present in many religions is synonymous with this absolute truth of how the universe works, it's an unattainable standard that we all try to aim for, intuitively and knowledge-wise, or seek wisdom from, and I believe the intention of prayer is to have a way to get closer to it, to tune the self to the god in order to sense and know this absolute truth more clearly. I reckon that conceptually, prayer and meditation are very similar in this sense, but with a focus on different areas of the self.
> All our moral codes and social norms we live by are purely human invention
I'd say much of it is selected for by evolution. e.g. Most animals don't fight or kill more than they need to since this not optimal - risk of personal injury and depleting a food resource (there are exceptions of course such as the fox in the henhouse), and this is so universal it seems it has to have a genetic basis.
>All our moral codes and social norms we live by are purely human invention, derived from things that happen to work to produce a somewhat functioning society.
Religious people would disagree, and the evidences are all to see, hear and ponder from their sacred holy books. But the main questions are that which holy books are truly sacred, meaning that purely and utterly God words. Otherwise it's human innovation/exaggeration or something in between (i.e corrupted God words).
Based on these holy books, religious people adhere and follow their prophets teachings and actions to the best of their ability since the prophets are their fellow human beings and not angels send from the heavens.
Ooh thanks for the hijack! It’s so much easier to talk to someone who isn’t stuck in a picture of the brain from the 1980s.
Yes, I fall more towards the camp that a lot of our cognitive models, built from times when we didn’t have the resolution of understanding we have of the capacity of even a single neuron, and before we knew how astrocytes played a role, suffer from being an abstraction describing an abstraction. They are not tethered in the dynamics of the molecules and cells that give rise to the behavior, but rather from an interpretation of observed behavior.
This paper from the field of chronobiogy is one I’d recommend that helpfully contrasts this:
>In circadian research, the models are not proposals regarding the basic architecture of circadian mechanisms; rather, they are used to better understand the functioning of a mechanism whose parts, operations, and organization already have been independently determined. In particular, circadian modelers probe how the
mechanism’s organized parts and operations are orchestrated in real time to produce dynamic phenomena—what we have called dynamic mechanistic explanation.
And what you’re describing, the enactivist description of cognition, (and 4E cognition more broadly as a framework), is one way the neuroscience community is trying to move past these issues.
Two things give me confidence these are the right track:
1. Circadian rhythms are evolutionarily ancient. Bacteria have em. Plants have em. But different molecular tools shape very different clocks, though the same 24 hour cycle is being tracked.
2. The way these rhythms are generated is not through some central system that broadcasts the information to other regions. Instead, it’s instantiated in every cell in the body, and the behavioral rhythm is due to the synchrony between cells. Resonance absolutely plays a role, and has been well documented. The brains role, via the suprachiasmatic nucleus or SCN, is to orchestrate this synchrony, but it is not the source of the rhythms.
3. This slow rhythm definitely regulates cognition (time of day effects in learning, memory formation, recall etc are well documented), but turns out, the molecular mechanisms by which the clock responds to light hugely overlap with the molecular mechanisms of learning in the synapse, and even more recent work has shown clock proteins are actually in the synapses and synaptic activity affects the clock.
All this points to nested oscillators, and even better, because this is all grounded in actual molecular dynamics, there’s plenty of falsifiability.
Clock disruption, depending on how you do it, has huge impacts on time perception, cognition, memory, aging AND consciousness.
Obviously I’m biased (also did chronobiology in school), but hopefully I’ve left you curious. Happy to answer more questions all this may have set off.
Thanks for your response, very intriguing! I have some controls background, and there’s something tantalizing about the idea that perhaps we need to be looking at the brain in frequency space, as it were. Are you aware of reservoir computing and do you see it playing a part in this?
Yes I’ve come across reservoir computing. As a neuroscientist, it made me sit up and take notice.
I’d say that I feel there’s homology in language. What reservoir computing says about the efficiency benefits of having a fixed but tunable dynamics to use as an underlying reservoir feels very adjacent to how I intuitively think of brain function.
The key thing from the circadian field you’ll appreciate:
The biological clock is a limit cycle oscillator. You have a bunch of chemical reactions that have negative feedback and some feedforward arms, and together they create a dynamical 24-regime. About 40-60% of the transcriptome of any given cell shows circadian dynamics.
Now this gives you phase, and an internal temporal reference for all your functions. In chronobiology, you call this the organisms subjective time. The system is chemically partitioned not just physically but over time, and behavior results from the dynamical interactions underneath which are concerned with anticipating solar and lunar periodicities in the environment, since those are so very common and determinative to fitness in many niches.
Note the fact that it is subjective time but has an objective description. However, external measurement without the background of the chronotype accounted for will thing of a lot of variance as “noise”.
My own philosophical conclusion has been that this is the source of our confusion with consciousness. We don’t account for the internal causal order of events, which are timed, and with cross frequency coupling and phase-amplitude linkages begging to be worked out with real world data.
I wonder the effect of diary writing on the brain, I see always recommended as either part of the self-improvement space or meditation. But reading your comment, I wonder if it allows to strengthen memories, especially if ones reread it after a while. I must experiment, now it's easier than ever with LLMs
"His memory was so powerful that he could still recall decades-old events and experiences in the smallest details. After he discovered his own abilities, he performed as a mnemonist; but this created confusion in his mind. He went as far as writing things down on paper and burning it, so that he could see the words in cinders, in a desperate attempt to forget them. Some later mnemonists have speculated that this was a mentalist's technique for writing things down to later commit to long-term memory. Reportedly, in his late years, he realized that he could forget facts with just a conscious desire to remove them from his memory, although Luria did not test this directly."
I believe that one need to have superhuman memorization abilities to have definite confusion due to too much remembered. More trivial explanation of this effect in normal ageing persons is age-related brain shrinkage.
> Obviously the brain is not a computer,
Our brain consists of approximately 86 billions quantum computers [2] controlling tens-of-thousands chemical neural networks with at least 10 coefficients, communicating [4] using lasers [5] (coherent light is laser light).
I suspect the anxiety of being unable to forget things has an inherent selection bias. You will only stress over the things you can't forget, the things you have forgotten you won't stress over...
Generally claims of eidetic memories are overstated, doubly for older claims, but Nigel Richards memorized a French dictionary in 9 weeks, over 6k words a day, x2 including the alphagram.
I consider myself to have a decent memory, but that is 200x what I'd think myself capable of, assuming I want to retain it all at the end of the 9 weeks.
Our brain consists of approximately 86 billions quantum computers [2] controlling tens-of-thousands chemical neural networks with at least 10 coefficients
By that logic, a Blackwell GPU contains 208 billion quantum computers.
Each transistor is a quantum mechanical system and takes hundreds of model parameters to describe. So apparently a GPU is a 208-billion-node quantum supercomputer.
"The brain is not a computer" != "Parts / aspects of the brain do computation".
"A computer" in the first statement is IMO obviously intended in the common usage sense of the term, i.e. the brain is not a desktop computer or smartphone, or Turing machine, or etc, and thinking of it like these things will cause more error than insights. Also, billions of interlinked mini bio quantum computers arguably produce something with emergent properties and behaviour much, much more complex than "a computer". I am with GP, the analogy to "a computer" is not super helpful here unless you highly restrict the meaning.
But yeah, the Shereshevsky case is a super interesting one, thanks for linking!
You are wrong, as I didn't limit the analogy specifically to desktop computers only, try reading the rest of what I wrote. The original analogy was obviously hedging against simplistic brain-computer analogies, so the response demanding to "live with that" was misguided and arrogantly wrong.
Multiple other responses also point out the definition given leads to absurdities / triviality (the liver is a computer in this context), so the "correction" with citations is both inept and inapt.
Well, sure, it's a computer of sorts, although in the abstract sense of being able to perform computations so is our liver, so perhaps not a very useful concept.
What I meant (as I assume you realize) was "not a von Neumann architecture computer", but I'd also fairly confidently assert that it's not a quantum computer either.
Our ANN model of a neuron is obviously too simple (especially being a synchronous model, not a real-time asynchronous one), but it's hard to imagine that all of the classical chemistry, let alone quantum, details are important. It's necessarily built out of chemistry, but selection is happening at the level of behavior - presumably depending only on a much higher level set of abstract capabilities (ability to learn, etc), not the exact details of chemistry.
The success of LLMs, a crude prediction mechanism built atop a crude ANN, does tend to support the idea that low level details don't matter. Timing will matter if we want to go beyond LLMs to AI that can learn time-based things and not just sequence order, but how much else will matter remains to be seen!
Mostly analog, with some threshold-triggered functions and lots of weighted adders and maybe multipliers.
> but how much else will matter remains to be seen!
Indeed, and the similarities between the brains we were born with and the brains we build is tantalizing. I'm really curious about the next chapters of this all.
I don't think there is a "full" state, but watching the changes as the years go by I have thought of collisions. Not really like a hash table, though--most collisions are with things that would be filed together. I think it's more like the brain returns a random item matching the category. Brains are expensive, memory that gets fuzzy with age is probably the better choice than trying to feed a brain that does better.
Yes, evolution is not going to penalize everyone with a "100-year brain" when the rest of our body seems optimized for something more like a 30-40 year lifespan. Evolution is all about propagating your genes, and not too many 100-year olds are going to be doing that!
In any case, fuzzy associative recall is really the "design spec" for a brain, so semi-graceful degradation and fuzzy recall comes for free.
I'm not sure if old-age memory unreliability is totally random - I would not expect it to be. I'd expect these associative recall failures to happen when there are too many memories partially matching the same "key" (activation pattern), which presumably happens mostly when they do have something in common.
I feel like I feel this increasingly as I age. I used to be able to remember nuanced details of API and computer systems. Now, so many of those memories simply get stored as “FLAG: double check the docs” (because I’ll forget).
I’m a lot better at assessing things from a high level, knowing where to find the information I need, and knowing how to assess it for accuracy - but I can’t remember details anymore. Books become summaries. Trips become a few key snapshots representing the trip.
I see it differently: as a teen, I could remember the exact word for lots of things, I could recall numbers and data with high precision. Nowadays, I'm pretty bad at all those things, but I can very well understand metaphors and connects concepts from wildly different topics — which I couldn't back then.
I see this kind of "interconnecting ideas" of my current years as somewhat connected with "fuzzy memory". It's like, when I retrieve data, it's not as precise, and sometimes I get neighbouring data, but that can actually of use.
You may have lost your superhuman ability to recall the script of every single episode of a pop cartoon with 20 seasons but you have gained the superhuman ability to make dad jokes.
Am neurobiologist and I don't buy the "full" argument, its much more likely a brain aging thing.
The brain, before your born, in GW25 (gestational week 25) has finished growing all the neurons you're basically [we can talk about this later perhaps] going to have for the rest of your life. At GW25 current estimates say you have ~86 billion neurons. Now the timing for this next part is a bit unsure but your body doesn't need anywhere close to 86 billion neurons, so at some point as early as early adulthood you start to lose 85,000 neurons per day pretty steadily until you die. Now neurons are not the end all be all because connections are potentially what really matter but that is to say that the neurons that you have after GW25 are the neurons you basically have. Now going onto synaptic connections where things start to matter more. Now synapses form and then get pruned all the time its natural. But the rate of formation and the rate of pruning is not the same at all times of life. Now from a raw number of synapse scale we see a tipping point at around 16-26 years old (debated hence the big range) where the number of synapses start to go down, indicating that the rate of pruning is now outpacing the rate of formation. [It does seem however that the dysfunction of the rate of pruning i.e. not enough ends up with consequences like schizo or asd {autism}]. There is another factoid that the rate of decline seems to stay relatively stable until you hit ~60 and then synapse related decline becomes much more noticeable and we start to think of synapse loss as exponential.
Now about new neurons after GW25 is a whole topic in of itself... heavily debated but I won't get into that history, the most accepted viewpoint is that it does exist in the hippocampus [other regions as well] (really cool work with c14 carbon dating from atomic bomb {2010} and perhaps less cool new sequencing methods give evidence {2025/2026}). Note estimates of how many are born are comparatively low-ish, 500-1000 neurons per day.
That begs another question however which is why and what do they do? Final interesting part is that when you stop the mouse hippocampus neurons from dividing, [unethical to do in humans :( ] distinct representations of experiences start to look similar and overlap. I also know if you ablate neurogenesis in mouse nasal cortex I think mice lose the ability to form new sensory sensations all together.
Disclaimer, the evidence for function of new born neurons is actually pretty low only a few studies have tried this so its no where close to accepted and thus far far from textbook standard so take it as you will.
But couldn't, and wouldn't you expect, both things to be true?
Existing memories may be degrading as a result of ongoing neuron death and synapse pruning, but at the same time our "storage capacity" (neurons+synapses) is essentially fixed or decreasing, and new memories are going to increasingly be competing with existing ones.
Presumably there is at least some connection between our neuron + synapse counts and species longevity. If we had evolved to live longer then one might naively expect us to have more neurons/synapses to support life-long learning and memories, although you could also argue that, for example, episodic memories too far back are not useful to remember, so maybe there is a cap to how much episodic memory capacity we need, and the complexity of the evolutionary niche we inhabit also limits the amount of declarative knowledge we need to store.
It's been noted in birds that hippocampus size seems highly correlated with the species memory capacity - but specifically for memory of seasonal seed hiding locations, where the bird only needs memory capacity for the number of seeds (can be 10's of thousands) hidden in a single season - they presumably reuse this hippocampal memory capacity each season, and forget the last season.
Have there been any studies relating brain size / memory capacity to the need of a species for lifelong memory?
My theory behind this is that when you encounter something new/novel it is distinct enough to remember -- where you were when you learned of 9/11; the days around the start of covid/lockdowns; being in a car accident; getting married; etc..
However, when you experience the same thing repetitively -- having breakfast; getting ready for school/work/etc. -- it doesn't make sense for your brain to remember each of these events as distinct events but to mush them together into a single or combined memory. This may be due to how the brain indexes/references memories and that memories with the same references get combined together.
This is also partly why time appears to go faster when you get older -- the same day-to-day events blend together but the key milestones (holidays, etc.) stick out.
How much of this, I wonder, is a function of the fact that our circadian rhythms get less robust with aging. The circadian clock hugely influences learning and memory processes, gating when you can learn and how much, and shaping the storage and recall also.
We know that with age the amplitude of these rhythms can decrease, as can the synchrony between cells.
This kind of mid-management seems to have at least some circadian component, and it would have been great if they looked to see if the effects were equally bad at all times of day, and knew the chronotype of the participants to use as a reference. I’d love to see if every test participant was tested at their cognitive peak, too.
Worth keeping in mind also is that memories are not a reliable facsimile of objective reality.
Instead, they are selective representations of parts of a subjective experience. It also means that memories aren’t static, but shift over time and can even be imagined.
Being an "aging brain" myself I can totally relate to this. I've been a relatively obsessive photographer (i.e. visual diary) since digital cameras were practical, i.e. over 25 years now. 238K photos in the collection so far, and I can actually find things in it.
And I've more than once found that an anecdote I've been telling has been incorrect, precisely in a "melded memories" kind of way. It happened at this sort of gathering which were usually held for that reason so X was involved... and then finally look at the old pictures and there's no sign of X. It is literally the brain invoking "lossy compression" to make it all fit, just like certain web services that let you upload pictures without limit might reduce old ones, that hardly anyone ever looks at any more, to lower quality to limit storage bloat.
Of course any married man can confirm that this only applies to men. Women's memories are flawless. If they claim that they remember a conversation exactly, word for word, 20 years later, who's to prove them wrong?
Just throwing out an idea: I recently stumbled upon an old-school website where someone is uploading a photo of their day (almost) every single day since 1998.
Came across screen captures of the original Everquest (SO COOL) and other games, photos of anime/cosplay events, city shots, family, friends, etc. Really fun for them, and fun for people who like me who still enjoy surfing the web.
Everquest looked sooooo good back then. Man! No more Meridian 59!
Why did you have to destroy my memories? :)
It's like looking at the first DivX encoded movie that was soooooo good vs. VideoCD/LaserDisc. And then you look at it today and it's so damn blocky and also in that tiny resolution. Ugh!
> Of course any married man can confirm that this only applies to men.
My first read of this headline was to observe that people seem to get more comfortable stereotyping people different from them as they age. Thanks for the datum.
I think that's because memories really are highly compressed, with the average complex memory (of a grand day out, for example) being compressed into at most a paragraph of text's worth of information. Everything else is made up of context.
For example, having no photographic memory, I can remember the general "feel" of the innards of an antique shortwave receiver I used to have as a kid. But almost all of that is context. I know what canned octal base vacuum tubes and big tuning capacitors look like, what sort of components were in the big band selector "sled" (for geeks: This was an NC2-40D) and so on. So I can "zoom in" on the coarse memory by just filling in details that are generic, not specific. And these details may be from different sources than the original memory too.
And so the brain needs to remember - if one is not faceblind, which in fact I am - just the overall geometry of someone's face, and all the other stuff can be filled in as needed (how they look when they smile or are anxious etc.) Compression, compression. We have a lot to learn from nature. Note that a faceblind person does this too, of course - just not with a geometrically exact visualization of that person's face, just one with the correct "feel".
And that's also why synthetic memories can be formed so easily. You really don't have to insert much. The brain infers the rest as needed.
Given the number of neurons, hash collisions are inevitable? /s
Our model of the world, and this includes "memories" is very highly compressed, and extremely lossy. We reconstruct everything from what neurons activate when we recall something, so it can also be like a corrupted zip, where we don't access everything required in the moment. This can be affected by sleep, stress, hormones, etc.
What is quite interesting is when we try and recall something and can't, but the subconscious processing continues and you recall it later.
>I don’t think the sexist comment at the end is appropriate for HN
I don't think the ageist comment in the headline is appropriate anywhere.
It is well known that memories and perceptions at every age are largely constructed more as "what makes sense" ("no, a gorilla did not run across the basketball court") than "as recorded" recordings.
Perhaps the rate of occurrence changes, but everybody does this at every age. When even our experience is an interpretation of the real world, our memories are even less accurate.
On average, obviously. Any given day ranges from zero, to a couple hundred for especially interesting days. Also a huge blip when the kids were young. Runs of "zero" days end up being dark ages in long term memory. Sure, went to work, ate, slept etc. but nothing interesting. That's where the "at least one interesting picture for Mom" thing provided a baseline.
Anecdatum: I confidently told a woman that her son, who was looking for a research opportunity in Berne, Switzerland, was heading for the birthplace of the World Wide Web, which is actually a couple of hours west and south at CERN.
The concept of memories blending together resonates after watching a recent Kurzgezadt video entitled "How Are Memories Stored Inside Your Brain?". It makes the point that the very act of recalling a memory changes it:
> But memories are not static like photos. Like dioramas made from wax, each time they’re under the spotlight of your attention they can melt and change a tiny bit.
I suspect my oldest memory, of me getting into a booster car seat as a child, is no longer the actual memory, but the accumulated imaginings of me trying to remember that specific memory, masquerading as a memory.
Comet Hale-Bopp is definitely a case of this for me. Although I remember a few interesting things about the process of seeing it, I have no idea of what it looked like to me.
I can remember the neighbors standing slightly in our yard and then explaining to my parents the comet and pointing it out to us. I can remember at some points that followed being aware of the parallax effect as it seemed to appear on either side of my other neighbors’ house as I walked up and down the driveway.
But just a year or so later, I knew that the picture in my memory was more like an illustration of a comet. The accurate image was completely gone even though the memories surrounding it remained, and in the place of that image was something that I had imagined.
I was positive that I remembered an old hobbit woman scowling and calling Gandalf a storm crow, in the films. Then I found a clip of the scene and it just wasn't in there. It was actually Théoden who said that. But the version in my head is better, so if I make such mistakes please don't correct me.
136 comments
[ 0.22 ms ] story [ 63.3 ms ] threadHigh definition holographic memories of every experience in our lives (every cognition) are trapped in our minds, most humans simply cannot access them.
Ordinary minds rely upon “referential” memories, and every time the mind cognates over a memory, it makes a new referential memory of that cognition.
Aging brains have more and more referential memories, as well as general atrophy, loosing coherence with their original.
There are exercises which one can do which will develop and enhance the exploration of holographic memory, without neurotic compulsive modifications (it’s a bad habit). The mind is an advanced technology we use poorly.
Ignore my account if you wish. The handling of memories as the corruption through conflation and obfuscation of the original is widely accountable.
The part of the holographic memory is unexplored for obvious reasons (beyond modern scientific reach.) Many individuals extraordinary behaviors may be explained by the lack of neurological access by others, not information storage availability.
In contrast, when we forget something we are not summarizing anything, we are just losing the ability to recall something. We might remember what we have forgotten later on, showing that it is still somewhere in the mind, and that the reason we have forgotten it is not necessarily because it has been discarded.
Even then, what this article is talking about is not even normal forgetting as a function of the brain. It's talking about a degenerative and dysfunctional form of memory loss where different memories are confused with each other.
But, she's come around on that, and recently been upset about the affair he's been having (they divorced in 1980), and is irate over him having another kid with his new wife (1983), but isn't surprised that I haven't spoken to him in 12 years. She's never surprised at how I've aged, but doesn't remember her grandkids even exist and insists on meeting them soon (but remembers them when she sees them). Two weeks ago, she called to yell at me for things I did in high school (1990). At this point, I seem to be the anchor for everything recent, but when I'm not around physically, it's scattered everywhere else.
My mom & aunt took yearly turns keeping her in their homes. If you kept her away from the stove, she still seemed quite happy helping fix her own breakfast, taking her meds, etc. Give her a warm washrag after, and she'd wipe down the table and then proceed to putter all around the kitchen, wiping and straightening. She preferred cartoons on TV (easier to see, I guess), and was always glad to fold clean towels or clothes, if she had the chance.
Once, she actually recognized me, and remembered my name. It was a very precious 5 minute chat, but then the memories were gone. Still, I'll always remember her that way, because it was just like how I remembered her, when I was a child.
Mine is showing signs that several of us believe to be dementia, but she refuses to be seen and screened. It has gotten worse over time - repeated conversations, sometimes inside the same phone call, and a shift from "can't remember things if she doesn't write them down" to "needs to ask on multiple occasions because she hasn't remembered to write it down" - but not to the point where she seems to be a danger to herself.
I love my mother, but she is difficult. She also has grown with age. While she is difficult now, she used to be abusive and manipulative. My father would never speak ill of her, but from some things she's said I believe she was worse before she was raising us. If my mother regresses to an earlier state of mind, we will both need professional help. She will need professional care, and I will need help to not internalize the way she treats me.
Then again, i'm 45 and I've been losing my keys and my IDs since I was 20
The entire article is AI-generated.I wish such sources would include the prompting used as well as the model so a (human) reader can better evaluate the document.
There is a responsible signer.
If you think their method can be improved, write at https://studyfinds.com/contact/
They disclose an "AI policy" - https://studyfinds.com/ai-policy/
> This process includes multiple checks using different LLM platforms, human editorial verification, and cross-referencing with original sources to ensure accuracy and eliminate false information, misleading content, fabricated content, bias, and unreasonable speculations or editorialization
I recently had a very curious experience - my in-dream me recalled a memory from a past dream. It was a really strange feeling that definitely added to the "realism" of the dream.
It seems that memories must be stored as embeddings with single multi-neuron assemblies (cortical columns?) storing multiple embeddings as a kind of contents-addressable memory that is able to keep memories distinct due to the very high dimensional space (# neurons per assembly) being used. However, you'd expect that at some point if you store too many memories in a single assembly the recall accuracy is going to go down.
You'd expect that with big brains being so costly, evolution has only equipped us with brains big enough to store a lifetime of memories, so it would be odd if memory didn't suffer as we get old.
To make a computer analogy, it's a bit like a hash table getting too full. Say you had a hash table without any overflow mechanism... up to a point recall may still be pretty good, but as the table gets closer to full there will be more hash collisions and likely hood of "false recall". Obviously the brain is not a computer, but the analogy may hold up reasonably well if you consider the hash table keys and values as embeddings and the store operation being an embedding merge rather than overwrite.
Engrams, that is, the physical trace of a memory, are not stable through life. They start out in the hippocampus, but as the stimulus recedes in time without reinforcement, it moves away.
No evidence exists though that the memory is encoded in one set of cells. This spatial segregation of memory is the worst hangover from the “brain is a computer” analogy. Even if it is, why in the world would it be like our digital devices which specifically have the Von Neumann bottleneck? In biology, memory and processing are not segregated.
There’s growing evidence the memory is much more distributed over the network, and is recomposed based on salience overlap with a new stimulus.
Another factor to keep in mind is circadian rhythms. There’s growing evidence for how much the memory system and timekeeping system overlap, at a molecular level. Every neuron (and other cell) has an intrinsic clock that ticks at roughly 24 hours, and continues to do so even in total darkness.
When you encode the memory has a lot to say, based on your chronotype, on how and how well you will remember it. Same with learning: there’s a time of day based variation.
Sleep, and dreaming, is when these memories seem to get replayed and critical features and connections are incorporated into the system and its regime, awaiting the right triggers to access a state similar to when the memory formed.
I’m stitching across a lot of different research, and I want to be clear many aspects of this system are not yet fully worked out.
But what we do know points to a system that works with different physical and algorithmic priors, and the dynamics are sharply distinct from current digital computers.
There are some hints that increased memory access times scale with the amount of information the brain has stored vs the more typical narrative that aging decreases the capabilities of the brain.
> Our results indicate that older adults'; performance on cognitive tests reflects the predictable consequences of learning on information-processing, and not cognitive decline. We consider the implications of this for our scientific and cultural understanding of aging.
[0] https://pubmed.ncbi.nlm.nih.gov/24421073/
Obviously not, which is why I didn't say it did!
However, if you want to identify where long-term memories are stored, then that is in the cortex, but it should go without saying that this doesn't make the cortex the storage component of a von-Neumann architecture!
> No evidence exists though that the memory is encoded in one set of cells
I'm not sure what you are trying to say.
Memories are presumably stored as embeddings - a distributed representation, and an episodic memory may well be stored as "chained together" episodic "scenes/chunks" where each chunk recalls the next.
However, a distributed representation isn't the same as a holographic one, and any redundancy may well still be localized within given cortical columns, so I think you may be wrong if you are saying that individual memories/chunks are not confined to one set of cells (some localized neural assembly such as a cortical column).
But you sneak it into your assumptions on what a memory must be.
> However, if you want to identify where long-term memories are stored
And why do you assume there is a specific “where” for the memory?
> then that is in the cortex
There’s good deal of evidence disproving this. The cortex is involved in sensing, and yes, the sensory information associated with a memory will recruit appropriate cortical cells. This doesn’t mean the memory resides in the cortex.
> I'm not sure what you are trying to say.
Let me restate what I’m saying then:
There are four claims bundled together in the way you were describing biological memory: that a specific ensemble is activated when a given memory forms; that it’s the same ensemble that gets activated over time when that memory is retrieved; that it’s spatially compact, like a column in region of the brain; and that this ensemble it’s dedicated to that memory, or similar memories .
The first is well supported. An engram, a network of neurons, is indeed activated when a memory first forms, and gets stabilized due to repeated stimulus. Re-activating these neurons in a different context can make the subject (a mouse) behave as it would if it had contextual signals to evoke said memory.
However: 1. This engram is not in one particular part of the brain. There’s a cortical part that overlaps the sensory regions that were involved. But plenty of other regions are part of the engram 2. There’s turnover, over the course of weeks, when the specific cells involved in the engram drift, while the behavior remains stable. 3. The same synapses participate in many memories.
> Memories are presumably stored as embeddings
No. Let’s consider songbirds, which are an excellent worked out example (in an animal with no cortical columns at all, by the way).
What’s learned is a temporal sequence, with neurons in a nucleus in their brains each firing one brief burst at a fixed point in the motif, so the content of the memory is its dynamics rather than any value. The circuit that evaluates the match against the tutor template is the same circuit generating the output being evaluated. Song degrades overnight during sleep replay and recovers the next day, and in seasonal species the song nuclei change size across the year with neurons added and lost while the song persists. There’s no read that leaves the item untouched, no persistent address, and no substrate holding still. “Stored as” imports all three.
And it goes below the neuron or synapse. Hearing a tutor song drives immediate early gene expression that habituates with familiarity, and singing drives large transcriptional changes in the song nuclei that differ by social context for the same motor output. Since transcription runs on minutes to hours and the proteins turn over, any persistent state has to be actively regenerated rather than deposited.
TLDR: the memory isn’t a static store. There’s no “location” for it, distributed or otherwise, though specific locations can be in the chain that’s activated for retrieval/productuin. Instead, memory, over time, is driven by a dynamical regime that adjusts its dynamics to account for the temporal pattern in the salient stimulus.
Nothing, down to the epigenetic changes in the DNA of these neurons, can be seen as “the” location of “a” memory, especially over time.
> if you are saying that individual memories/chunks are not confined to one set of cells (some localized neural assembly such as a cortical column).
That is indeed the case.
> and that this ensemble it’s dedicated to that memory, or similar memories
No - that's the exact opposite of what I said. My whole point was that a cortical column is NOT dedicated to a single memory (we'd run out of memory!), but rather acts as an embedding space containing many (sparse) embeddings.
Due to the size of the embedding space and sparsity of individual embeddings, there is little chance of much overlap between embeddings and therefore associative recall is reliable. When there are too many memories stored using the same set of neurons, then there will be non-trivial overlap between embeddings (this is the definition of "too many" / "full") and associative recall becomes unreliable.
Note incidentally that this explanation holds regardless of whether distributed embeddings are stored in a more localized area (or areas - visual, auditory, etc components) or more globally distributed. At the end of the day evolution has equipped us with a right-sized brain, and an individual that outlives the useful life it is adapted for can expect to experience memory failures.
I'm not sure why you bring up bird brains, and specifically bird songs(!), but FWIW it seems that their short term memory likely works similarly to our own in as much as it is based on the hippocampus, with a very strong correlation between bird hippocampus and memory capacity (ability to memorize 10's of thousands of hidden seed locations in some species). Some birds such as crows certainly have long term memory where I'd guess those may have migrated to their pallium, but we're discussing human memory here (or at least I thought we were).
Yes, like basic reading without digging into details. You must have heard about HM, since you’re saying all this. But here’s the facts:
When H.M.’s remote memories were probed carefully, they turned out to be gist-like and semanticized, not vivid re-experiencings of specific events. Here’s the paper:
https://pubmed.ncbi.nlm.nih.gov/15716139/
Only semantic memory of the episodes can be said to be “cortical” (though please note, lack of hippocampus doesn’t mean lack of other brain regions…). Rich recall absolutely does require the hippocampus.
Once again, please try not to “spherical cow” the complexity of the brain to try and fit it into your analogy to digital computing. You will get an underdermined model that will miss the subtleties, and lead you to claims that are poor fits for the reality.
As for why I brought up bird brains… there part of the same evolutionary web. Is there some reason you want them excluded? They’re a well studied model for a fairly complex memory task, using substantially smaller neurons more densely packed in a different architecture than mammals.
In cognitive science, as in computer science I’d imagine, it’s useful to look at the full picture before making strong claims.
The bird case is interesting because the region of interest is a nucleus, rather than cortical columns, and actually has well documented structural variations in size, as well as gene expression, over the seasons, while the memories are forming.
If your model is correct, it needs to account for those facts.
Let me make it simple for you.
We have a finite number of neurons in our brain, as do birds, and our brain is attempting to store an ever growing number of memories in those. And, no, this is not a digital computer (is your reading comprehension really so bad?).
Nobody, including you, knows exactly where different types of memory are stored, and for my argument it makes no difference. What does make a difference is how they are represented, which I am suggesting is sparse embeddings.
So far, you've been ignoring my actual argument and instead responding to various strawmen of your own making, so it's not clear if you even understand what a sparse embedding is.
If you do understand, then it should be obvious that it makes no difference whether the neurons comprising this embedding space are in the hippocampus, cortex/pallium or anywhere else. Clearly you do NOT understand, since you bring up bird brains (pallium vs cortex) and want to argue about location of storage (hippocampus vs elsewhere) as if it made a difference to MY argument.
My argument (if you care to respond to it, which so far you have not) is that when sparse embeddings have little to no overlap, then associative recall by a similar pattern will work reliably, but when multiple embeddings have too much in common then recall will suffer as multiple embeddings will match.
Hint: if you think this has anything to do with digital computers then you have misunderstood and need to go back and re-read more carefully, or google for any terms you do not understand.
Let’s say I’m a complete moron and don’t know what a sparse embedding is.
Pretty please, can you define it for me and then tell me, in detail, where in whatever region of the brain you think this is going on… how is it going on?
Explain how “memories must be stored as embeddings with single multi-neuron assemblies (cortical columns?) storing multiple embeddings as a kind of contents-addressable memory”
You have moved past the cortical column. But still seem to be insisting it’s a bunch of neurons, somewhere… or has that also conveniently changed? Whatever your current position is, please go ahead and explain what components of what cells or otherwise are involved in this process you’re describing.
An entity embedding doesn't need to use all the dimensions of the space it is positioned in - some dimensions may be unused (sometimes implemented as a coorrdinate of 0 in that dimension). These are called "sparse" embeddings. For example, an LLM's tokens are represented as embeddings in what is typcially an approximately ~1000 dimensional space, but start out as sparse embeddings just representing a short letter sequence (but then go on to be transformed/augmented with additional information and so become less sparse).
As an example, let's say an embedding space has 10 dimensions, then a couple of sparse embedding examples could be:
These two embeddings have no overlap (where both are non-zero), and the more dimensions you have the more likely it is that two random sparse embedding will have little in common.Embeddings are used in many types of artificial neural networks, not just LLMs, for example face recognition networks, where they are trained such that similar faces (multiple photos of the same person) are close together in the embedding space, and post-training you can then "look up" any arbitrary photo (in the training set or not) by embedding it and seeing what is nearby in the embedding space, which will be similar looking faces.
Presumably real neural networks used embeddings in a similar way, since, for example, it obviously requires many neurons to represent the many differences between different faces, and there is going to be overlap between the neurons used to represent multiple faces (this is not a computer with one storage location for face #1, and a different location for face #2).
A neural network, real or artificial, uses groups of neurons (e.g. a cortical column) to represent an embedding space, with each neuron corresponding to a dimension. A single group of neurons (column) can store multiple embeddings (e.g. faces) represented as different activity patterns (which neurons are firing), and if these are sparse embeddings then the firing patterns corresponding to different memories stored in the same column will have little in common.
Now, I don't know how you believe associative recall is implemented in the brain - how does someone's voice, or half obscured face, recall their entire face, so feel free to imagine it as implemented however you will, but I'd suggest that in an assembly such as a cortical column that when a set of synaptic inputs are triggered the assembly as a whole will learn to reactivate the entire pattern when only part of the original set of synaptic inputs are triggered, and this is the basis of associative recall. There are papers that suggest exactly how this may work given the cortical column microcircuit.
So, with all that said, the suggestion I was making for why (or at least one reason why) memory degrades with age, with memories blending together, is that with a finite quantity of "storage" (cortical columns) you will eventually be storing so many memories (absent a deliberate forgetting mechanism) that there will inevitably be overlap between the sparse embedddings, and this associative recall will therefore not cleanly recall individual memories but rather recall blended memories according to what they have in common.
Obviously some types of memory are at least initially stored in the hippocampus, so no reason to focus on cortical columns, but I expect the use of embeddings is universal.
https://authors.library.caltech.edu/records/znzhp-4j547
Faces live in a ~50-dimensional continuous space (25 shape axes, 25 appearance axes). They measured about 205 neurons across 2 macaques (human studies have substantiated much of this, some from the same lab), and the key thing is: every neuron participates in every face.
The paper shows faces are embedded, but as points in a dense linear space where neurons are axes, not as sparse activity patterns where neurons are on/off slots.
The mapping between the neuronal activity and the facial structures is invertible. Record these same cells, and their firing pattern can be used to reconstruct the face. Or, if you generate a novel face, you can predict the firing rates of these neurons for it. As far as I understand, this doesn’t work for sparse embeddings.
Some cells carry the shape coordinates and others carry the appearance coordinates, in a heirarchy.
There’s an embedding space, yes. But that space isn’t defined by a network of “on” and “off” neurons. The embedding space is instead constructed by the activity of neurons, and the differences in activity distinguish the faces, using the same set of neurons.
And distance in the ensemble activity of these neurons tracks the distance in face space.
If faces use sparse embeddings, you wouldn’t expect similar faces to evoke similar activity would you? Yet that is exactly what this paper shows, and the same has been shown in the human brain for faces.
There are places where it’s sparse activity of a subset of neurons that maps to specific memories. What you’re describing is what you’d see if you look at how the dentate gyrus (part of the hippocampus) handles your memories in the same location.
But even there, the sheer number of cells makes this combinatorially such a vastly overdetermined system for a lifetime that there’s no capacity limit of the kind you’re describing. Even 1% of these cells lighting up for a specific memory leaves you with so many possible combinations that you’d have to live for a few million years to be in the right scale to at least being to talk about capacity issues.
The brain just isn’t capacity limited by the number of neurons the way your intuition is pointing you.
If you say this has nothing to do with the Von Neumann bottleneck or computational functionalism, fine, but how do you square that with the statement below, which you made further down responding to another post?
> but it's hard to imagine that all of the classical chemistry, let alone quantum, details are important. It's necessarily built out of chemistry, but selection is happening at the level of behavior - presumably depending only on a much higher level set of abstract capabilities (ability to learn, etc), not the exact details of chemistry. The success of LLMs, a crude prediction mechanism built atop a crude ANN, does tend to support the idea that low level details don't matter. Timing will matter if we want to go beyond LLMs to AI that can learn time-based things and not just sequence order, but how much else will matter remains to be seen!
It’s really odd to see these two paragraphs, because the second actually tells you why your first is wrong.
Simply put, the biochemistry is timed. I urge you to study how temperature compensation of circadian rhythms is achieved. That anticipatory function goes all the way down to the molecular level.
It might go down to the quantum level too. In birds, magnetoception depends on a protein called cryptochrome IV, which uses a singlet born, entangled radical pair of electrons to sense the very weak magnetic field of earth.
Now cryptochrome 4 is bird speci...
Well, my analogy was relating computer hash tables collisions to sparse embedding collisions, so what you are discussing now is my suggestion itself, not the analogy, which is fine!
The study we're discussing was nominally about associative recall, not faces per-se, and specifically about the hippocampus not the cortex (that Macaque face study).
> Memory accuracy for pairing faces with objects and scenes dropped sharply
https://studyfinds.com/aging-brains-blend-memories-together-...
That said, I wouldn't be so sure that face embeddings are fully dense, even if they are not particularly sparse either, given that not all faces have the same set of features, such as facial hair, glasses, blemishes, etc. OTOH, it's possible, perhaps likely, that similar faces are stored together, in which case they may be more dense.
> If faces use sparse embeddings, you wouldn’t expect similar faces to evoke similar activity would you? Yet that is exactly what this paper shows, and the same has been shown in the human brain for faces.
With embeddings in general, sparse or not, you'd expect individual dimensions/neurons to represent different axis of variability, so you would expect individual neurons to be active for multiple different faces that are similar along that same axis. Note that the study you are citing used individual neuron recordings as well as fMRI, but of course we don't currently have the ability to simultaneously record from the hundreds of neurons that are likely being used to embed faces, so I don't think this study has much to say about the degree of sparsity of these embeddings. Obviously IF faces both with and without glasses are stored in the same embedding space (same set of neurons), then one would expect the "glasses neuron" not to be firing for a face without glasses, which would confirm some degree of sparsity.
> The brain just isn’t capacity limited by the number of neurons the way your intuition is pointing you.
It's highly unlikely that our brains are wasteful and have unused capacity - this recalls daft pop-sci articles saying that we only use 10% of our brain ... We know that brains and memory do degrade with age, and the only question is how - maybe the encoding mechanism itself is failing resulting in embeddings that have more overlap than they should (or one could hypothesize a dozen other possble failure modes). Do you have any theory that explains the "aging brains blend memories" study that we're discussing, at the level of detail of the hippocampal patterns they are seeing?
> It’s really odd to see these two paragraphs, because the second actually tells you why your first is wrong.
> Try as you might, you can’t separate out the deep linkages from the molecular to the behavioral in biology.
Of course the linkages are there since our brain is built from chemistry, yet selection pressure is happening at a much higher functional level. The part of my response you are referring to is addressing the question of how much of this molecular level detail needs to be retained in an ARTIFICIAL neuron model sufficient for it support the same phenotype-level functional behavior, and the answer is we just don't know, because nobody has yet tried to do it.
Prior to LLMs a lot of speculation about what is necessary in the brain to learn language, e.g. Chompysky-ian language-organ nonsense, might have sounded logical and compelling, but now we have proof-by-existence that "prediction is all you need". We're going to need to wait until we have built an artificial brain, capable of learning time-based phenomen...
> A neural network, real or artificial, uses groups of neurons (e.g. a cortical column) to represent an embedding space, with each neuron corresponding to a dimension.
You:
> The paper shows faces are embedded, but as points in a dense linear space where neurons are axes, not as sparse activity patterns where neurons are on/off slots.
So you are saying that neurons are axes (aka dimensions), exactly as I just said!
> If faces use sparse embeddings, you wouldn’t expect similar faces to evoke similar activity would you?
Yes, of course you would, because that is precisely how embeddings work, and how you recognize someone even though their head is turned or they are wearing a baseball cap or whatever.
This is the ENTIRE point of emebeddings and why evolution has discovered them as a way of representing things and a way to recall them. You may have seen someone a million times, and yet the sensory patterns your visual cortex is fed are likely different every single time because they are not in the exact same orientation, making the exact same facial expression, with the exact same haircut, etc, etc, etc.
To your brain these are merely similar inputs, similar faces, but there is only so much facial variation between individuals, and if the input is similar along dozens or hundreds of axes of variability (i.e. close in embeddign space) then it is alomst certainly the same individual.
Note that "recall keys" (embeddings) are typically sparse even any stored embedding is not, since the face you are looking at may indeed be turned left or half obscured, and this partial/sparse pattern needs to recall the full one.
How can you be a neuroscientist, or even self-identify as one, if you are not already familiar with things like embeddings, and are making such basic 100% wrong assumptions as "you wouldn’t expect similar faces to evoke similar activity" ?!!!
Would delay line memory be a fair analogy? Information stored in a delay line memory never stays in one place. I think you are saying biological information is "stored" in the amplitude and phase of oscillations of our cells. Is that correct?
I'm still inclined to believe there is some form of static store, which would be required for inherited memories. Things like our (and many other mammals) ability to recognize emotions in others. Somehow our gametes encode what a happy, sad, or scared face looks like. I instincts as inherited memories.
I guess my point is the brain not being "von Neumann" in architecture or digital is not proof that isn't a "computer" of some sort.
Capacity in what sense? Are we saying it’s X MB of data the brain can store? That claim is steeped in assumptions.
On the other hand, no one is claiming the brain has infinite memory or anything. And it’s certainly not a very accurate memory system. I’m arguing against “capacity” being understood as “these specific physical components located here and here we can ID store memories, and can get crowded with too many memories” sense.
This most particularly fails because not all memory is even identical in the brain, whether we mean the physical changes associated, the topology of the information, or how it’s activated.
> There is no reason to believe distributed memory doesn't suffer from the capacity component of the bottleneck.
I didn’t know there was a capacity component to the bottleneck, only a bandwidth one.
All I’m trying to say is that analogy to current typical memory storage systems to explain the brains memory processes is not helpful.
> I guess my point is the brain not being "von Neumann" in architecture or digital is not proof that isn't a "computer" of some sort.
Indeed, since the word computer was first used for humans. But what kind of computer matters enormously. Ising machine? Quantum+classical stack? Reservoir computer? All those frameworks have processes in the brain they can point to as homology.
Which points to a possibility: maybe the brain is multiple types of computers interacting. And the physical realization of these computing architectures aren’t spatially separated but thread through each other in the biochemistry and physical dynamics of cells.
I wonder to what extent evolution selects for longevity (& graceful degradation) past a certain point? I would think that once you are past breeding age it's generally more beneficial for the species if you die off and make way for the next generation, other than having some residual benefit as babysitters for the grandkids and leading the herd to the water source in the next drought.
I’ve been consuming a lot of talks / writing recently about “enactive” pictures of how our brains function. From what I gather, recent studies have called into question the entire idea of real world concepts being “represented” by an area of the brain at all. While it’s true that atandard fMRI-style snapshots of brain activity are semi-stable over the course of a short experiment, it’s not true over longer timeframes. The response to the same stimulus will change over time. They refer to this as “representational drift” in the literature, and some people are using this to bolster theories of mind that they consider non-representational. They instead emphasize the brain as a kind of dynamical system that learns to “resonate” with the world to pull itself back into homeostasis. The focus shifts away from facts and memories as data, and sees neuronal plasticity more as a mechanism for tuning the brain’s resonant frequencies. This obviously places high importance on the spiking, recurrent nature of actual neurons, as opposed to the neurons-as-functions / back-propagation / ML approach.
My mind’s not made up on how interesting and revolutionary this approach is / isn’t. The distinction seems to be about whether learning is more like “writing to disk” or “tuning a PID controller” - but in either case, the world is leaving a stateful imprint on your brain that will impact how it processes future data. Is that important to understanding how brains work, or is it just semantics?
It's akin to how the saying goes, that when we argue, we need to reach the same wavelength where the other one is to reach an understanding.
In communication studies or sociology or linguistics or one of those fields, there's the idea that communication is about making pacts about meanings, and finding the common ground to understand and delimit the message.
In a sense, from that basing, one could argue that understanding can be seen as an act of harmonizing. I think there's something universal in it.
I'd say much of it is selected for by evolution. e.g. Most animals don't fight or kill more than they need to since this not optimal - risk of personal injury and depleting a food resource (there are exceptions of course such as the fox in the henhouse), and this is so universal it seems it has to have a genetic basis.
Religious people would disagree, and the evidences are all to see, hear and ponder from their sacred holy books. But the main questions are that which holy books are truly sacred, meaning that purely and utterly God words. Otherwise it's human innovation/exaggeration or something in between (i.e corrupted God words).
Based on these holy books, religious people adhere and follow their prophets teachings and actions to the best of their ability since the prophets are their fellow human beings and not angels send from the heavens.
Yes, I fall more towards the camp that a lot of our cognitive models, built from times when we didn’t have the resolution of understanding we have of the capacity of even a single neuron, and before we knew how astrocytes played a role, suffer from being an abstraction describing an abstraction. They are not tethered in the dynamics of the molecules and cells that give rise to the behavior, but rather from an interpretation of observed behavior.
This paper from the field of chronobiogy is one I’d recommend that helpfully contrasts this:
https://www.sciencedirect.com/science/article/abs/pii/S00393...
>In circadian research, the models are not proposals regarding the basic architecture of circadian mechanisms; rather, they are used to better understand the functioning of a mechanism whose parts, operations, and organization already have been independently determined. In particular, circadian modelers probe how the mechanism’s organized parts and operations are orchestrated in real time to produce dynamic phenomena—what we have called dynamic mechanistic explanation.
And what you’re describing, the enactivist description of cognition, (and 4E cognition more broadly as a framework), is one way the neuroscience community is trying to move past these issues.
Two things give me confidence these are the right track:
1. Circadian rhythms are evolutionarily ancient. Bacteria have em. Plants have em. But different molecular tools shape very different clocks, though the same 24 hour cycle is being tracked. 2. The way these rhythms are generated is not through some central system that broadcasts the information to other regions. Instead, it’s instantiated in every cell in the body, and the behavioral rhythm is due to the synchrony between cells. Resonance absolutely plays a role, and has been well documented. The brains role, via the suprachiasmatic nucleus or SCN, is to orchestrate this synchrony, but it is not the source of the rhythms. 3. This slow rhythm definitely regulates cognition (time of day effects in learning, memory formation, recall etc are well documented), but turns out, the molecular mechanisms by which the clock responds to light hugely overlap with the molecular mechanisms of learning in the synapse, and even more recent work has shown clock proteins are actually in the synapses and synaptic activity affects the clock.
All this points to nested oscillators, and even better, because this is all grounded in actual molecular dynamics, there’s plenty of falsifiability.
Clock disruption, depending on how you do it, has huge impacts on time perception, cognition, memory, aging AND consciousness.
Obviously I’m biased (also did chronobiology in school), but hopefully I’ve left you curious. Happy to answer more questions all this may have set off.
I’d say that I feel there’s homology in language. What reservoir computing says about the efficiency benefits of having a fixed but tunable dynamics to use as an underlying reservoir feels very adjacent to how I intuitively think of brain function.
The key thing from the circadian field you’ll appreciate:
The biological clock is a limit cycle oscillator. You have a bunch of chemical reactions that have negative feedback and some feedforward arms, and together they create a dynamical 24-regime. About 40-60% of the transcriptome of any given cell shows circadian dynamics.
Now this gives you phase, and an internal temporal reference for all your functions. In chronobiology, you call this the organisms subjective time. The system is chemically partitioned not just physically but over time, and behavior results from the dynamical interactions underneath which are concerned with anticipating solar and lunar periodicities in the environment, since those are so very common and determinative to fitness in many niches.
Note the fact that it is subjective time but has an objective description. However, external measurement without the background of the chronotype accounted for will thing of a lot of variance as “noise”.
My own philosophical conclusion has been that this is the source of our confusion with consciousness. We don’t account for the internal causal order of events, which are timed, and with cross frequency coupling and phase-amplitude linkages begging to be worked out with real world data.
It's a computer, it's just not a digital computer
But even digital computers can have stuff like processing-in-memory. "Computers" doesn't need to mean whatever architecture usually run as of 2026
[1] https://en.wikipedia.org/wiki/Solomon_Shereshevsky
"His memory was so powerful that he could still recall decades-old events and experiences in the smallest details. After he discovered his own abilities, he performed as a mnemonist; but this created confusion in his mind. He went as far as writing things down on paper and burning it, so that he could see the words in cinders, in a desperate attempt to forget them. Some later mnemonists have speculated that this was a mentalist's technique for writing things down to later commit to long-term memory. Reportedly, in his late years, he realized that he could forget facts with just a conscious desire to remove them from his memory, although Luria did not test this directly."
I believe that one need to have superhuman memorization abilities to have definite confusion due to too much remembered. More trivial explanation of this effect in normal ageing persons is age-related brain shrinkage.
Our brain consists of approximately 86 billions quantum computers [2] controlling tens-of-thousands chemical neural networks with at least 10 coefficients, communicating [4] using lasers [5] (coherent light is laser light). Live with that. ;)Generally claims of eidetic memories are overstated, doubly for older claims, but Nigel Richards memorized a French dictionary in 9 weeks, over 6k words a day, x2 including the alphagram.
I consider myself to have a decent memory, but that is 200x what I'd think myself capable of, assuming I want to retain it all at the end of the 9 weeks.
By that logic, a Blackwell GPU contains 208 billion quantum computers.
Each transistor is a quantum mechanical system and takes hundreds of model parameters to describe. So apparently a GPU is a 208-billion-node quantum supercomputer.
"A computer" in the first statement is IMO obviously intended in the common usage sense of the term, i.e. the brain is not a desktop computer or smartphone, or Turing machine, or etc, and thinking of it like these things will cause more error than insights. Also, billions of interlinked mini bio quantum computers arguably produce something with emergent properties and behaviour much, much more complex than "a computer". I am with GP, the analogy to "a computer" is not super helpful here unless you highly restrict the meaning.
But yeah, the Shereshevsky case is a super interesting one, thanks for linking!
No one has ever thought that a brain is a desktop computer, and that is not the common usage sense of the term in this context.
Multiple other responses also point out the definition given leads to absurdities / triviality (the liver is a computer in this context), so the "correction" with citations is both inept and inapt.
Well, sure, it's a computer of sorts, although in the abstract sense of being able to perform computations so is our liver, so perhaps not a very useful concept.
What I meant (as I assume you realize) was "not a von Neumann architecture computer", but I'd also fairly confidently assert that it's not a quantum computer either.
Our ANN model of a neuron is obviously too simple (especially being a synchronous model, not a real-time asynchronous one), but it's hard to imagine that all of the classical chemistry, let alone quantum, details are important. It's necessarily built out of chemistry, but selection is happening at the level of behavior - presumably depending only on a much higher level set of abstract capabilities (ability to learn, etc), not the exact details of chemistry.
The success of LLMs, a crude prediction mechanism built atop a crude ANN, does tend to support the idea that low level details don't matter. Timing will matter if we want to go beyond LLMs to AI that can learn time-based things and not just sequence order, but how much else will matter remains to be seen!
Mostly analog, with some threshold-triggered functions and lots of weighted adders and maybe multipliers.
> but how much else will matter remains to be seen!
Indeed, and the similarities between the brains we were born with and the brains we build is tantalizing. I'm really curious about the next chapters of this all.
In any case, fuzzy associative recall is really the "design spec" for a brain, so semi-graceful degradation and fuzzy recall comes for free.
I'm not sure if old-age memory unreliability is totally random - I would not expect it to be. I'd expect these associative recall failures to happen when there are too many memories partially matching the same "key" (activation pattern), which presumably happens mostly when they do have something in common.
I’m a lot better at assessing things from a high level, knowing where to find the information I need, and knowing how to assess it for accuracy - but I can’t remember details anymore. Books become summaries. Trips become a few key snapshots representing the trip.
I see this kind of "interconnecting ideas" of my current years as somewhat connected with "fuzzy memory". It's like, when I retrieve data, it's not as precise, and sometimes I get neighbouring data, but that can actually of use.
The brain, before your born, in GW25 (gestational week 25) has finished growing all the neurons you're basically [we can talk about this later perhaps] going to have for the rest of your life. At GW25 current estimates say you have ~86 billion neurons. Now the timing for this next part is a bit unsure but your body doesn't need anywhere close to 86 billion neurons, so at some point as early as early adulthood you start to lose 85,000 neurons per day pretty steadily until you die. Now neurons are not the end all be all because connections are potentially what really matter but that is to say that the neurons that you have after GW25 are the neurons you basically have. Now going onto synaptic connections where things start to matter more. Now synapses form and then get pruned all the time its natural. But the rate of formation and the rate of pruning is not the same at all times of life. Now from a raw number of synapse scale we see a tipping point at around 16-26 years old (debated hence the big range) where the number of synapses start to go down, indicating that the rate of pruning is now outpacing the rate of formation. [It does seem however that the dysfunction of the rate of pruning i.e. not enough ends up with consequences like schizo or asd {autism}]. There is another factoid that the rate of decline seems to stay relatively stable until you hit ~60 and then synapse related decline becomes much more noticeable and we start to think of synapse loss as exponential.
Now about new neurons after GW25 is a whole topic in of itself... heavily debated but I won't get into that history, the most accepted viewpoint is that it does exist in the hippocampus [other regions as well] (really cool work with c14 carbon dating from atomic bomb {2010} and perhaps less cool new sequencing methods give evidence {2025/2026}). Note estimates of how many are born are comparatively low-ish, 500-1000 neurons per day.
That begs another question however which is why and what do they do? Final interesting part is that when you stop the mouse hippocampus neurons from dividing, [unethical to do in humans :( ] distinct representations of experiences start to look similar and overlap. I also know if you ablate neurogenesis in mouse nasal cortex I think mice lose the ability to form new sensory sensations all together.
Disclaimer, the evidence for function of new born neurons is actually pretty low only a few studies have tried this so its no where close to accepted and thus far far from textbook standard so take it as you will.
Existing memories may be degrading as a result of ongoing neuron death and synapse pruning, but at the same time our "storage capacity" (neurons+synapses) is essentially fixed or decreasing, and new memories are going to increasingly be competing with existing ones.
Presumably there is at least some connection between our neuron + synapse counts and species longevity. If we had evolved to live longer then one might naively expect us to have more neurons/synapses to support life-long learning and memories, although you could also argue that, for example, episodic memories too far back are not useful to remember, so maybe there is a cap to how much episodic memory capacity we need, and the complexity of the evolutionary niche we inhabit also limits the amount of declarative knowledge we need to store.
It's been noted in birds that hippocampus size seems highly correlated with the species memory capacity - but specifically for memory of seasonal seed hiding locations, where the bird only needs memory capacity for the number of seeds (can be 10's of thousands) hidden in a single season - they presumably reuse this hippocampal memory capacity each season, and forget the last season.
Have there been any studies relating brain size / memory capacity to the need of a species for lifelong memory?
However, when you experience the same thing repetitively -- having breakfast; getting ready for school/work/etc. -- it doesn't make sense for your brain to remember each of these events as distinct events but to mush them together into a single or combined memory. This may be due to how the brain indexes/references memories and that memories with the same references get combined together.
This is also partly why time appears to go faster when you get older -- the same day-to-day events blend together but the key milestones (holidays, etc.) stick out.
Supposing it is the submitted (under Antonaros), see https://news.ycombinator.com/item?id=49538906
We know that with age the amplitude of these rhythms can decrease, as can the synchrony between cells.
This kind of mid-management seems to have at least some circadian component, and it would have been great if they looked to see if the effects were equally bad at all times of day, and knew the chronotype of the participants to use as a reference. I’d love to see if every test participant was tested at their cognitive peak, too.
And the actual study: https://academic.oup.com/cercor/article/36/7/bhag114/8758582
Instead, they are selective representations of parts of a subjective experience. It also means that memories aren’t static, but shift over time and can even be imagined.
And I've more than once found that an anecdote I've been telling has been incorrect, precisely in a "melded memories" kind of way. It happened at this sort of gathering which were usually held for that reason so X was involved... and then finally look at the old pictures and there's no sign of X. It is literally the brain invoking "lossy compression" to make it all fit, just like certain web services that let you upload pictures without limit might reduce old ones, that hardly anyone ever looks at any more, to lower quality to limit storage bloat.
Of course any married man can confirm that this only applies to men. Women's memories are flawless. If they claim that they remember a conversation exactly, word for word, 20 years later, who's to prove them wrong?
Came across screen captures of the original Everquest (SO COOL) and other games, photos of anime/cosplay events, city shots, family, friends, etc. Really fun for them, and fun for people who like me who still enjoy surfing the web.
edit: Screencap of an Everquest screencap for funsies: https://imgur.com/a/HUqc6p5
Why did you have to destroy my memories? :)
It's like looking at the first DivX encoded movie that was soooooo good vs. VideoCD/LaserDisc. And then you look at it today and it's so damn blocky and also in that tiny resolution. Ugh!
My first read of this headline was to observe that people seem to get more comfortable stereotyping people different from them as they age. Thanks for the datum.
For example, having no photographic memory, I can remember the general "feel" of the innards of an antique shortwave receiver I used to have as a kid. But almost all of that is context. I know what canned octal base vacuum tubes and big tuning capacitors look like, what sort of components were in the big band selector "sled" (for geeks: This was an NC2-40D) and so on. So I can "zoom in" on the coarse memory by just filling in details that are generic, not specific. And these details may be from different sources than the original memory too.
And so the brain needs to remember - if one is not faceblind, which in fact I am - just the overall geometry of someone's face, and all the other stuff can be filled in as needed (how they look when they smile or are anxious etc.) Compression, compression. We have a lot to learn from nature. Note that a faceblind person does this too, of course - just not with a geometrically exact visualization of that person's face, just one with the correct "feel".
And that's also why synthetic memories can be formed so easily. You really don't have to insert much. The brain infers the rest as needed.
Our model of the world, and this includes "memories" is very highly compressed, and extremely lossy. We reconstruct everything from what neurons activate when we recall something, so it can also be like a corrupted zip, where we don't access everything required in the moment. This can be affected by sleep, stress, hormones, etc.
What is quite interesting is when we try and recall something and can't, but the subconscious processing continues and you recall it later.
I don't think the ageist comment in the headline is appropriate anywhere.
It is well known that memories and perceptions at every age are largely constructed more as "what makes sense" ("no, a gorilla did not run across the basketball court") than "as recorded" recordings.
EDIT: https://en.wikipedia.org/wiki/Inattentional_blindness#Invisi...
My grandmother has dementia, and if you know her you can see exactly which events/memories she's erroneously combining.
So that's an interesting parallel. Says something about memory.
https://www.youtube.com/watch?v=PqtggjVAi8M
> But memories are not static like photos. Like dioramas made from wax, each time they’re under the spotlight of your attention they can melt and change a tiny bit.
I can remember the neighbors standing slightly in our yard and then explaining to my parents the comet and pointing it out to us. I can remember at some points that followed being aware of the parallax effect as it seemed to appear on either side of my other neighbors’ house as I walked up and down the driveway.
But just a year or so later, I knew that the picture in my memory was more like an illustration of a comet. The accurate image was completely gone even though the memories surrounding it remained, and in the place of that image was something that I had imagined.