I feel like it’s 4 ~ 1 > 2 > 3. The example of CNNs seems like this, where the artificial neural networks and actual brains face similar constraints and wind up with superficially similar solutions, but when you look at all the tricks that CNNs use (especially weight-sharing, but also architecture choices, choice of optimizer, etc.) they’re not actually very biology-like, and were developed based on abstract considerations more than biological ones.
I feel like it’s 4 ~ 1 > 2 > 3. The example of CNNs seems like this, where the artificial neural networks and actual brains face similar constraints and wind up with superficially similar solutions, but when you look at all the tricks that CNNs use (especially weight-sharing, but also architecture choices, choice of optimizer, etc.) they’re not actually very biology-like, and were developed based on abstract considerations more than biological ones.