  The work of McCloskey and Cohen popularized the concept of catastrophic
interference. They used a neural network that tried to learn addition using two
groups of examples as two different tasks. In their case, learning the second
task rapidly deteriorated the acquired knowledge about the previous one. We
hypothesize that this could be a symptom of a fundamental problem: addition is
an algorithmic task that should not be learned through pattern recognition.
Therefore, other model architectures better suited for this task would avoid
catastrophic forgetting. We use a neural network with a different architecture
that can be trained to recover the correct algorithm for the addition of binary
numbers. This neural network includes conditional clauses that are naturally
treated within the back-propagation algorithm. We test it in the setting
proposed by McCloskey and Cohen and training on random additions one by one.
The neural network not only does not suffer from catastrophic forgetting but it
improves its predictive power on unseen pairs of numbers as training
progresses. We also show that this is a robust effect, also present when
averaging many simulations. This work emphasizes the importance that neural
network architecture has for the emergence of catastrophic forgetting and
introduces a neural network that is able to learn an algorithm.
