Hebb’s Law can be represented in the form of two rules:If two neurons on either side of a connection (synapse) are activated synchronously, then the weight of that connection is increased.If two neurons on either side of a connection (synapse) are activated asynchronously, then the weight of that connection is decreased.Learning according to Hebb’s Law is primarily consistent with one of the following kinds of learning. Reinforcement learning Un-supervised learning. Supervised learning

What is the role of the learning rate (η) in the Hebb rule?Select one:a.It determines the convergence of the weight updateb.It determines the speed of the weight updatec.It determines the size of the weight updated.It determines the direction of the weight update

How is the Hebb rule used in the training of a neural network?Select one:a.It is used to determine the structure of the neural networkb.It is used to calculate the output of the neural networkc.It is used to adjust the weights of the neural network based on the input and outputd.It is used to determine the input to the neural network

What is the main idea behind the Hebb rule?Select one:a.Neurons that fire together, wire togetherb.Neurons that fire in opposite directions, wire togetherc.Neurons that fire at the same time, wire togetherd.Neurons that fire in the same direction, wire together

In Hebbian learning, what does the weight adjustment depend on? a. Correlation between input and output b. The number of layers in the network c. The learning rate d. The transfer function used

Updating cycles for postsynaptic neuron outputs and connection weights in a Hebbian Learning Network.Step 1: Initialization: Set initial synaptic weights to small random values in the interval [0, 1].Step 2: Activation: Compute the postsynaptic neuron output Y j from the presynaptic inputs element X i j in the data-item X j : Y j = if ( i=1..n Sum Xi j*Wi j –T)>=0 then 1 else 0. T is the threshold value of neuronStep 3: Update the weights in the network. Wi j+1 = Wi j +a * Y j*Xi j where a is the learning rate parameterStep 4: Iteration: go back to Step 2Task: Consider a Neuron with inputs from 4 neighbouring neurons. The learning rate a = 1 and the threshold T = 1The weights are initiated as W1 = 1 1 1 1. The training data vectors are: X1= 1 0 0 1, X2 = 0 1 1 0 and X3 = 1 1 0 0.X1 1 0 0 1 W1  1 1 1 1 Y1 = 1 W2 = W1 + Y1 * (1 0 0 1) = 2 1 1 2…...................................................................................................... ?How will the final weight vector look like when the training data has been processed? 3 2 2 3 2 2 3 3 3 3 2 2 3 2 3 2 None of the above