Sorry, this was a jerk comment. I was probably annoyed by the tortuous exposition. Please eliminate the first 3 paragraphs. Real question: "there is no lower b

Sorry, this was a jerk comment. I was probably annoyed by the tortuous exposition. Please eliminate the first 3 paragraphs. Real question: "there is no lower bound for M like log(N)" I would challenge this statement because the number of neurons is like the number of gaussian mixture components, which limits resolution of the radix buckets. In order for it to work, the divergence between real and modeled distributions must meet an upper bound which decreases as the inverse of N, and you might say that the divergence lower bound decreases as the inverse of M (although not sure). So given any distribution that cannot be modeled with a gaussian mixture, M must scale with N.