Neural Nets vs Brains

Neural networks are not a model of the working of the human brain. They are based on an extremely simplified approximation of how neurons connect and function (which while conceptually similar is a terrible predictive model for biological neurons) and are connected together in ways that have absolutely zero resemblance to how complex nervous systems look in real animals. The burden of proof here is absolutely on showing how LLMs can model the human brain.

This is not correct, artificial NNs are not functionally related to neurons in the brain. Nothing like back-propagation has been observed in real neurons. The early layers of a CNN may be similar to early perceptrons in the brain, but beyond this any connection between the two is fantasy.

You need to study some actual neuroscience.some things to remember:- neurons are not linear.- neurons are not time-invariant.- neurons are not causal.- neurons and actual anatomical connectivity do not map well to electronics-inspired wiring diagrams. (see: dendritic arbor)- anatomical structure does not imply functional connectivity.- functional connectivity does not imply anatomy!- synaptic junctions respond more or less well to different neurotransmitters, all of whic

Neural networks are a simplification of our brains, they are not a replication of it. It is just a modeling method that was inspired by how human neurons work, that's it. It's not 1 to 1 or anything.

Real brains have a very different structure and composition. Neurons aren't monotonic, there are loops, etc.

I think it's becoming pretty clear that they don't. First, scientists uncovered many additional ways neurons interact with one another[1]. Second, it seems that individual neurons do way more computing than in the simplistic ANN models [2].[1]: https://en.wikipedia.org/wiki/Ephaptic_coupling[2]: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles

We're not simulating brain neurons:- real neurons are stochastic and communicate through spikes, artificial neurons can communicate real values efficiently- real neurons are more like automatons, they have a dynamic in time, learning happens as a continuous interaction with only its neighbors; artificial neurons are "static" (use discrete time) and implemented by forward and backward pass, and also can use nonlocal information- real neurons can't backpropagate, becau

Before we get wildly over-optimistic, as every HN thread on AI becomes, realize that neural networks have basically nothing to do with actual biological neurons. There are a bajillion things neurons do that neural networks do not do. And there are similarly many things that are a part of neural networks, that biological neurons do not do. In addition to the fact that neuroscientists still do not have a clear understanding of biological neurons: you cannot expect to reverse-engineer something you

Could this be closer to how the brain works? Have we ruled it brains using back propagation?

You might like this article titled "Could a Neuroscientist Understand a Microprocessor?": https://journals.plos.org/ploscompbiol/article?id=10.1371/jo...