2. Using Hebbian-type of learning rule to obtain a Hopfield network which can memorise the following two patterns: [1 -1 -1 1 1], [1 1 1 -1 -1] a) Draw the structure of the Hopfield network b) Clearly indicate the weights and biases in the Hopfield network Test the network with the following corrupted patterns to see what the outputs are. Are they the expected outputs? Why? [-1 1 -1 1 1], [-1 1 1 -1-1] What if the input pattern is [1 1 1 1 1]? Can you make this pattern converge to either the first and second training pattern? Explain how this can be done. (7 marks)