In your slide - why is back propogation a further stretch from a true bio-NN than an ANN without back propogation?
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.
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.