Hacker News Reader

For reference this recent article describes research that tries to nail down the exact computational ratio https://www.quantamagazine.org/how-computationally-complex-i... spoiler : 5-8 layers of artificial neurons per complexity of biological neuron

View on HN

That's how many it takes to "represent" a neuron. But what does that mean and is it relevant? I haven't checked the original research, but if it means to model the results of a neuron, then it's not relevant (unless you're trying to do brain uploading). Two ANNs with different activation functions will need a lot of nodes to model a single node of the other network, but they have comparable computational power. We don't need to match the neurons to match the computational abilities.

What's the theoretical basis of this though? I don't have a theoretical background in computability, but in physics and there the complexity of a system very much correlates with the number of degrees of freedom. When simulating something, you want a bijection of the state of the simulation to the state of the system you are simulating, so you need to match (or exceed) the number of degrees of freedom. So if you know how many parameters you need to simulate another system until the simulation works, you can estimate how many internal degrees of freedom it has (degrees of freedom that are relevant to whatever you are measuring of course).

Reply on news.ycombinator.com