  In this report, we present a theoretical support of the continual learning
method \textbf{Elastic Weight Consolidation}, introduced in paper titled
`Overcoming catastrophic forgetting in neural networks'. Being one of the most
cited paper in regularized methods for continual learning, this report
disentangles the underlying concept of the proposed objective function. We
assume that the reader is aware of the basic terminologies of continual
learning.
