> 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 sp

> 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.