Sorry but this is the dumbest thing I've heard in the entire internet today. So basically what they did was they trained a deep neural net to predict the behaviour of a simulated rat neuron. Then they claimed that the number of layers and units in the deep neural net is somehow a measure of the complexity of a "biological neuron".

First of all, they didn't try to model the behaviour of a biological neuron, only of a model of a biological neuron, and an incomplete model at that. The article points that out itself.

More importantly, the claim rests on the assumption that a deep neural net architecture is somehow a sensible measure of the complexity of a completely unrelated process (as far as I can tell, deep neural nets are not in anyway related to simulated rat neurons). But this is a big, huge, gigantic assumption.

Suppose I train a bunch of humans to predict the behaviour of a simulated rat neuron. Suppose I find out that I need to train at least 1000 humans to accurately predict the simulated rat neuron's behaviour. What does that mean? That 1000 human brains are as complex as a simulated rat neuron?

Come to that- can a single human brain predict the behaviour of a simulated rat neuron? I'm prepared to wager that, no, it can't, because human brains are rubbish at prediction tasks like that. What does that mean? That a simulated rat neuron is more complex than a human with an entire human brain?

The entire premise of the work is absurd nonsense and it probably only got a mention in Quanta because it's something released by DeepMind. Irritating.