An ANN still resembles major features of an bio-NN.

1. A network

2. Flow of information is mainly unidirectional through a node

3. Multiple inputs, but one output, which is connected to the inputs of other neurons.

4. The connection strength between 2 neurons can be changed.

5. Non-linear behavior.

After all, I think, this is not such a bad first approximation. Hence the picture in the middle.

But I cannot believe that we learn by comparing thousands or millions of input and output patterns and back propagate the error through the network to perform a gradient descent at the neurons. That is simply not, what our brain does.

When there is feedback in neurons, what do you think that conveys?

I agree it is not some simple error correction like what is propagated backwards, but it happens often and I presume its something useful or it wouldn't be there.

Top down predictions are likely mediated by feedback connections from higher to lower areas. Functions include possibly encoding a generative prior for prediction, speeding up inference. They also play an important role in coding more informative error signals than simple derivatives and are part of how the brain learns even as it predicts.