Note: In this question you should assume that the activation function of a perceptron is the step function. More specifically, this function:

outputs 0 if the weighted sum of inputs is LESS THAN 0 (not less than or equal).

outputs 1 if the weighted sum of inputs is greater than or equal to 0.

Is it possible to design a neural network (which could be a single perceptron or a larger network), that satisfies these specs?

Takes a single input called X, which can be any real number.

If X < 3, the network outputs 0.

If 3 < X < 7, the network outputs 1.

If X > 7, the network outputs 0.

We don't care what the network outputs when X = 3 or X = 7.

If your answer is no, explain why not. If your answer is yes, your solution should include a drawing of the network, that shows the weights of each input of each perceptron, and that also shows which perceptron outputs are linked to which perceptron inputs.