It's somewhat important to consider the inputs, because if you want to make a classifier that can classify "inside circle vs outside circle" but the network needs to derive the nonlinearity itself, then you end up needing a more complex network

Eg on the playground^, see how many neurons you need to train a circle without using more than x1 and x2?

And yet, if you give the network x1^2 and x2^2, it can solve it with minimal additional neurons.

^ https://playground.tensorflow.org/#activation=tanh&batchSize...

With dendritic compartments, this seems like a waste of a perfectly good neuron that we could productively use elsewhere. ;)

Note that a SINGLE neuron can compute nonlinear functions like XOR.

Shameless plug: If anyone is interested, I did a post a while back on how neurons can act as logic gates:

https://blog.typeobject.com/posts/2025-neural-logic-gates/

This article builds on the first and creates a half adder out of neurons:

https://blog.typeobject.com/posts/2026-timing-is-the-bit/

Observation: 2 neurons, 2 wheels. One for each?