<p>In a recent paper, the authors designed a neuron they called <a href="https://arxiv.org/pdf/2108.12943.pdf" rel="nofollow noreferrer">Growing Cosine Unit(GCU)</a>:</p>
<p><a href="https://i.stack.imgur.com/o3bP7.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/o3bP7.png" alt="enter image description here" /></a></p>
solving XOR with single layer perceptron
<p>I've always heard that the XOR problem can not be solved by a single layer perceptron (not using a hidden layer) since it is not linearly separable. I understand that there is no linear function that can separate the classes. </p>

<p>However, what if we use a non-monotonic activation function like sin() or cos() is this still the case? I would imagine these types of functions might be able to separate them.</p>