To find the output value of the neuron, we first calculate the dot product of the weight vector and input vector: (0.2 * 0.2) + (-0.1 * 0.4) + (0.1 * 0.2) = 0.02. Since the bias is 0, the pre-activation value is also 0.02. With a ReLU activation function, the output is the maximum of 0 and the pre-activation value, which is 0.02.

Introducing non-linear activations is the best option for increasing the accuracy of the model. A neural network with only linear activations is limited in its ability to learn complex patterns. Non-linear activations, such as ReLU or sigmoid, allow the network to learn more complex relationships in the data, thus improving its performance and reducing underfitting.

A model predicting a patient's age from their brain MRI image requires a continuous output, as age is a continuous variable. One neuron with a linear output is the best choice, as it can produce a continuous output without any activation function constraints, unlike sigmoid, softmax, or Tanh.

The best output configuration for a model classifying a person's mood by their voice tone would be 'Multiple neurons with softmax'. This is because softmax function is used for multi-class classification problems. It gives the probabilities of each class and the sum of all probabilities is 1, which is ideal for mood classification as it can be angry, happy, sad etc.