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 adjust the weights of the neural network based on the input and outputc.It is used to calculate the output of the neural networkd.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

What is the main advantage of the Hebb rule?Select one:a.It is easy to implementb.It is able to handle nonlinear relationshipsc.It is able to handle large datasetsd.It is fast to converge

What is the equation for the Hebb rule?Select one:a.w(new) = w(old) + η(input - output)x(target)b.w(new) = w(old) + η(target - output)x(input)c.w(new) = w(old) + η(output)x(input)d.w(new) = w(old) + η(output - target)x(input)

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

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