[d] Can a network of linear activation perceptrons model non-linear functions?

Background: Math/CS. Took a course on ML in 2005. Re-reading [Mitchell](https://www.cin.ufpe.br/~cavmj/Machine%20-%20Learning%20-%20Tom%20Mitchell.pdf).

I'm confused about whether a network of linear activation perceptrons can model non-linear functions.

According to the book I'm reading (linked above), *every* boolean function can be represented by some network of interconnected units based on the [perceptron](https://en.wikipedia.org/wiki/Perceptron#Definition). This means that XOR, which is non-linear, can be represented by it. I found an [example](https://medium.com/@stanleydukor/neural-representation-of-and-or-not-xor-and-xnor-logic-gates-perceptron-algorithm-b0275375fea1) on the web that models XOR using a network of perceptrons (1/0 activations after a …

author book function functions linear machinelearning motivation multiple network non-linear perceptron sigmoid