Me: > 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

Me: > 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" ?!!!