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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 c

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: https://authors.library.caltech.edu/records/znzhp-4j547 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 specific and mammals don’t have it. Other cryptochromes are critical clock molecules. And the whole shebang of these evolved initially to be sensitive to blue light and repair DNA. Try as you might, you can’t separate out the deep linkages from the molecular to the behavioral in biology. Trying is perfectly fine for stuff like language models. But if you’re going to build models with internal time, best of luck if you ignore the molecular and the energetic considerations. Time emerges from the ground up, in biology, as in physics. Doubt we’ll get a free ride with computers.

HarHarVeryFunny 10d ago
> 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 comparing computer hash tables collisions to sparse embedding collisions, so what you are discussing now is my suggestion itself (pertaining to embeddings and recall), 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-instead-of-forgetting-them-study-finds/ 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 (e.g. eye color). 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 phenomena, and everything else our brain is capable of, to similarly be able to point at something (a future elaboration of an artificial neuron model), and then be able to say that this is the most that is needed. As far as this specific point - how much of the detail of a real neuron is functionally necessary vs how much of it is just a reflection of how it is built, you could also compare the massive complexity of something like a digital circuit transistor or logic component if you get down in the weeds and look at the specific gate architecture, and how it operates via quantum tunneling etc, or you could instead look at the functional behavior as a circuit component, and realize that none of it actually matters, and that transistors are interchangeable as long as they are functionally equivalent.

HarHarVeryFunny 10d ago

> 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 comparing computer hash tables collisions to sparse embedding collisions, so what you are discussing now is my suggestion itself (pertaining to embeddings and recall), 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-instead-of-forgetting-them-study-finds/ 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 (e.g. eye color). 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 phenomena, and everything else our brain is capable of, to similarly be able to point at something (a future elaboration of an artificial neuron model), and then be able to say that this is the most that is needed. As far as this specific point - how much of the detail of a real neuron is functionally necessary vs how much of it is just a reflection of how it is built, you could also compare the massive complexity of something like a digital circuit transistor or logic component if you get down in the weeds and look at the specific gate architecture, and how it operates via quantum tunneling etc, or you could instead look at the functional behavior as a circuit component, and realize that none of it actually matters, and that transistors are interchangeable as long as they are functionally equivalent.