The article asserts that on average they need ~1000 "neurons" to approximate one biological neuron. In the opposite direction, assuming we had manipulation tec

The article asserts that on average they need ~1000 "neurons" to approximate one biological neuron. In the opposite direction, assuming we had manipulation techniques create new neurons, position them and stretch out axons arbitrarily and control synapse linking strength (which we can't now), then IMHO there could be a 1-to-1 correspondence; the same octopus neurons that McCulloch-Pitts model tried to describe could also each approximate one artificial neuron for inference. Of course, learning is a different issue, brains don't exactly do back-propagation and gradient descent.