I’m not sure what you think I’m arguing against but saying that it matches with one of the cognitive models du jour is… odd.
The problem with the hopfield model is it simplifies the brain too much. The base unit is “the neuron”. Ok… but what about the Astrocyte? Mathematically you can write it as a different kind of neuron. Or ignore it. But why, as a biologist, must I buy this model which ignores the third partner of every synapse, which has an entirely distinct physical tiling architecture compared to neurons, and which are at a temporal offset from neurons?
Those facts about the brain are missing from the model from 1982. Which isn’t shocking since we didn’t know all this then.
Are you saying the brain is a Hopfield network, and that’s it? Because later you indicate otherwise. Kinda confused what I’m to make of it.
> Again, my impression is that the original author's idea was about capacity in general, then he gave an analogy. The analogy was wrong, but your reply went way beyond his specific analogy to the extreme of discarding memory itself as useful concept. To be clear, I agree that it is distributed and lossy and time-dependent. I agree it is not just a simple read-off of a static chunk with a fixed address.
Ok, but my argument wasn’t with the OP mentioning capacity, but with their analogy. Am I not allowed to break down that analogy with evidence?
> I would say that is both non-falsifiable and well-accepted.
Why’s it non-falsifiable? If you do find a single computational paradigm that explains all brain dynamics we can measure, then you have falsified the hypothesis that it’s an integration of multiple computational types.
That actual evidence already gives the notion credence doesn’t make it unfalsifiable in principle.
Went through the description. Doubt it’ll interest me. As a rule I’ve stopped giving too much time to models that predate the last decades actual mechanistic facts. They’re fun curiosities, but hard to take seriously anymore. Here especially, the absence of astrocytes in the picture makes it hard to buy they have anything real to say about the mechanics at play. Half the cells of the brain not in the explanatory picture is just too likely to fail.
(note: I’m certain astrocytes are mentioned as support cells, or maybe regulators… but we just know a lot more now due to new techniques that makes downgrading them like that questionable science to me)
> Ok, but my argument wasn’t with the OP mentioning capacity, but with their analogy. Am I not allowed to break down that analogy with evidence?
If someone makes an analogy between aspects of A and B, then there is an implicit assumed shared understanding that A & B are DIFFERENT, and what is being pointed out is that they nonetheless may be considered as having something, typically fairly abstract, in common.
Calling a horse's reins as analogous to a cars steering wheel doesn't mean that the person making the analogy can't tell a horse from a car - that would be a very dumb take. A reasonable criticism might be "it's an imperfect analogy since the reins also act as the brake".
If you disagree with an analogy, then you need to address the analogy, not do the dumb take of "a horse is not a car" ("a brain is not a computer").
So, yes, you're "allowed" to "break down" (criticize) the analogy, but please don't just do the dumb take. So, hash key collisions are not analogous to associative recall and sparse embedding overlaps in what way?
It's more about computational complexity it seems but maybe it cites stuff that might interest you.
It's using computational neural networks that no one has ever believed represent the biology of the brain, but I think I must still disclose the following to save the precious time of the genius solving the problem all by himself:
It doesn't fully account for every biological detail ever documented, so it's probably a meaningless "curiosity".
Oh it probably also doesn't account for every detail discovered since publication so even if had value at time of publication it is not worth reading now.
> Calling a horse's reins as analogous to a cars steering wheel doesn't mean that the person making the analogy can't tell a horse from a car
When a person says “I crashed the car because my steering wheel tore, like reigns tear”, they are overfitting their analogy, and it’s perfectly fine to point out the structural and physical differences that make the analogy useless for the question at hand.
You have dismissed the biology that shows the problems with your analogy as immaterial. And continue to insist it’s the right one for the question at hand. This is a pretty pickle, because no facts can shake you from your certainty that you're right.
You seem in love with your analogy no matter how incorrect it is. And I have no problem with that. But when you put half baked biological claims to support it, I’ll point it out. If that’s too much for you to bear, maybe come up with better analogies?
Where the hell did you get that from?! I mentioned the hash table / embedding space analogy precisely ONCE, in my initial post, and, just to try to avoid people like yourself being triggered by it, I even included "obviously the brain is not a computer".
But you still got triggered by it, still did the dumb take of "a brain is not a computer" (no shit - I just said that), then spent your entire energy on fighting your own strawmen and not once even responding to my actual suggestion.
Now, finally, you are asking "what is a sparse embedding"! Maybe next time don't bother responding to something if you don't even understand what is being talked about.
He hadn't even considered whether capacity was well defined in a distributed system. He has a brand spanking new theory that allows him to jump straight past abstractions and simplifications and "ancient 1980s views"[1] and "fun curiosities" like the basics of dynamics, structure, organization, and composition described in an ancient book (2000s) he couldn't possibly learn anything from because he's a biologist and knows better. Nevermind that it smells an awful lot like [2]. But he doesn't need to know anything about it it or acknowledge it because dummy non-biologists couldn't possibly help someone as smart as him.
[1] https://www.pnas.org/doi/pdf/10.1073/pnas.79.8.2554 a paper he confidentally rejected as meaningless, unaware and not interested in the overwhelmingly likely possibility that his current brand new ideas trace back to it.
[2] "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."
Hmm, I wonder if "we" figured that stuff out from individual and especially collaborative efforts combining abstractions and more theoretical approaches with expertise in biology, not to mention EE, biophysics, physics, and whatever else other fields that don't know about his favorite molecule and thus can be ignored if not shit on?
I mean with respect to your other thread too, where you seem to argue/believe academia has no idea about your brand new dynamical distributed view. I pointed to a really old paper with a self-admitted extremely simple model to point out that one of your specific insights over the field re. memory not being a memory stick is ancient and that your arrogance wrt being certain that your notion of memory is so novel that you weren't even sure there was a definition of capacity for distributed memory.
I intentionally gave older books and papers. I very clearly understand as does everyone in the field that they are not current or complete. But that awareness is precisely my point.
"Are you saying the brain is a Hopfield network, and that’s it? Because later you indicate otherwise. Kinda confused what I’m to make of it." You being confused by that is kind of my point, it shows the projection you're applying to the entire field. I understand models are wrong and iterative. I also understand the parts that survive, survive in the literature and the field at large. For example, the PNAS paper that showed "hey we think memory is distributed, right? Here's a simple model showing how memory might roughly be stored and recalled via these very simple dynamics over this very simple model. That's cool. Let's build on it." Did I make it clear enough yet that it was simple and wrong? Do you thus believe it wasn't valuable? I guess that would match your confident opinion that the whole field is dummies and of course those dummies cited an incorrect model so much.
If you stopped with saying "we know memory isn't a memory stick and here's the well-established evidence", then I'd fully agree with "Ok, but my argument wasn’t with the OP mentioning capacity, but with their analogy." and would've said nothing. Instead your posture was "I know some specifics based on recent findings that aren't obviously incorporated as of now into the current understanding -- which I refuse to read because it doesn't address precisely this thing only I understand(1) -- , so here is my theory that is certainly novel don't dare try to point me to preexisting literature that might help me refine my theory."
To be clear, I want everyone to explore their ideas and think it's great that you doubt things. But I don't like someone claiming superiority over a field they refuse to understand. And if your doubt something maybe you should check first if there's anyone else doubting it. (Hint: not only did everyone doubt it, they knew it was wrong and they're all actively working on using that doubt to improve it.) Of course a theory that predates a discovery doesn't cover that discover. Of course a simple model of memory isn't a complete model of memory, that was in fact the point! The notion of distributed memory and how it could feasibly work according to dynamics is made more precise than ever before to that time in that paper (among others). And of course it's outdated. But it contains a more precise statement and falsifiable statement of "memory is complicated dynamics and those dummies don't know and also that book is stupid because it doesn't cover X(2,3)" your theory espouses. My issue is with the arrogance and certainty you know best while refusing to actual understand what others think. And thus give no credit to. Science is iterative. Someone has an idea, builds a simple model as proof of concept. Someone else builds on that model precisely where it has holes (whose precise shape only present itself after new experiments motivated in part by the obviously wrong and simplified model and other attempts to break the model). Repeat. Built on the shoulders.. etc.
"Why’s it non-falsifiable? If you do find a single computational paradigm that explains all brain dynamics we can measure, then you have falsified the hypothesis that it’s an integration of multiple computational types.". I said non-falsifiable AND well-accepted. Non-falsifiable was the wrong word. What if I instead said trivially falsified and also adds nothing not known except to the layman who posits an analogy they clearly aren't sure of? Its non-falsifiable relative to the current state of the field, which I now know you're not interested in, because it says nothing not already stated more precisely, as of like 40 years ago.
(1) "As a rule I’ve stopped giving too much time to models that predate the last decades actual mechanistic facts. They’re fun curiosities, but hard to take seriously anymore. Here especially, the absence of astrocytes in the picture makes it hard to buy they have anything real to say about the mechanics at play. " In response to a book that has a lot to say about your new and novel dynamical theory.
(2) A hole it couldn't cover because the experiments and techniques didn't exist at that time. But they knew their theory wasn't complete. They were sharing what was learned up to that point especially the parts that seemed to generalize and survive specifics. That is, an incomplete model they knew was incomplete. But which I promise would help advance your journey.
(3) "Instead, memory, over time, is driven by a dynamical regime that adjusts its dynamics to account for the temporal pattern in the salient stimulus." Which is true and everyone in the field agrees with. And everyone agrees current models don't fully account for everything. Hence their continuing efforts to improve them by accounting for new findings.
The problem with the hopfield model is it simplifies the brain too much. The base unit is “the neuron”. Ok… but what about the Astrocyte? Mathematically you can write it as a different kind of neuron. Or ignore it. But why, as a biologist, must I buy this model which ignores the third partner of every synapse, which has an entirely distinct physical tiling architecture compared to neurons, and which are at a temporal offset from neurons?
Those facts about the brain are missing from the model from 1982. Which isn’t shocking since we didn’t know all this then.
Are you saying the brain is a Hopfield network, and that’s it? Because later you indicate otherwise. Kinda confused what I’m to make of it.
> Again, my impression is that the original author's idea was about capacity in general, then he gave an analogy. The analogy was wrong, but your reply went way beyond his specific analogy to the extreme of discarding memory itself as useful concept. To be clear, I agree that it is distributed and lossy and time-dependent. I agree it is not just a simple read-off of a static chunk with a fixed address.
Ok, but my argument wasn’t with the OP mentioning capacity, but with their analogy. Am I not allowed to break down that analogy with evidence?
> I would say that is both non-falsifiable and well-accepted.
Why’s it non-falsifiable? If you do find a single computational paradigm that explains all brain dynamics we can measure, then you have falsified the hypothesis that it’s an integration of multiple computational types.
That actual evidence already gives the notion credence doesn’t make it unfalsifiable in principle.
> https://mitpress.mit.edu/9780262041997/theoretical-neuroscie... might interest you
Went through the description. Doubt it’ll interest me. As a rule I’ve stopped giving too much time to models that predate the last decades actual mechanistic facts. They’re fun curiosities, but hard to take seriously anymore. Here especially, the absence of astrocytes in the picture makes it hard to buy they have anything real to say about the mechanics at play. Half the cells of the brain not in the explanatory picture is just too likely to fail.
(note: I’m certain astrocytes are mentioned as support cells, or maybe regulators… but we just know a lot more now due to new techniques that makes downgrading them like that questionable science to me)