Comment by randomImmigrant
10 hours ago
> 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.
https://www.pnas.org/doi/pdf/10.1073/pnas.79.8.2554 in a very precise sense going back a long time. I don't really know who you're arguing against or refuting? You seem to be touching on some pretty well accepted ideas.
"All I’m trying to say is that analogy to current typical memory storage systems to explain the brains memory processes is not helpful." 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.
"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 would say that is both non-falsifiable and well-accepted.
https://mitpress.mit.edu/9780262041997/theoretical-neuroscie... might interest you and on the other end bialek's spikes shows quite a bit actual does happen near the single neuron limit. bialek also does/did a lot of other stuff on capacities and processing you might be interested in. https://mitpress.mit.edu/9780262181747/spikes/
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.
> 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)