There are many statistical distributions and power laws in nature. The distance between neurons doesn’t have to follow a normal distribution any more than it co

There are many statistical distributions and power laws in nature. The distance between neurons doesn’t have to follow a normal distribution any more than it could follow the long tail distribution. The other common distributions are often overlooked by fixation on bell curves in data analysis. Anyways, this tendency seems to continue throughout the body in neural nexuses. And even outside the body in how we communicate our thoughts in social circles. I also wonder if language itself is deeply related to this structure. Zipf’s law relates to the long tail. Semantic space models also often show clusters of words and symbols. We have synonyms for most symbols we speak about, and often antonyms, but rarely are we able to think about something that’s about in the middle. But a LLM could have parameters such that it would produce semantically unrelated words “in the middle” if it was poorly fitted. In other words, if it was bad at producing language. So clearly, there is a property of at least language-based neural nets that they are necessarily related to the long tail probability distributions. Perhaps many more things than we commonly think are on a long tail distribution curve when it comes to neural nets, real brains, and our society. If only the normal distribution wasn’t so intoxicating to the academia and we paid other probability distributions the same amount of attention…