1. A neuron k receives input from other five neurons, as 0.5, 1, 0.25, -1.25 and -2. The corresponding synaptic weights are -0.7, 0.2, -0.4, -0.2 and 2.1. Assume the bias as -1.

1. Draw the computational model of neuron k.
2. Calculate the linear output of the neuron.
3. Find the output using Mc-Culloch Pitts model in activation function.
4. Find the output using Sigmoid function, whereas the slope parameter is 2.

2. Construct the following.

1. A fully recurrent network with 6 neurons, but with no self-feedback.
2. A fully connected feed forward network has 6 source nodes, 3 hidden layers, one with 4 neurons, second with 3 neurons and the third with 2 neurons, and a single output neuron.

3. What are the types of activation function describe with neat diagram.

4. Compare between supervised learning reinforcement learning and draw the diagrams.

5. Write about Boltzmann Learning rule with its mathematical models and its observations of equation