In this paper we explore the concept of sequential learning and the
efficacy of global and local neural network learning algorithms on a
sequential learning task. Pseudorehearsal (a method developed by
Robins  to solve the catastrophic forgetting problem which arises
from the excessive plasticity of neural networks) is significantly more
effective than other local learning algorithms for the sequential task.
We further consider the concept of local learning and suggest that
pseudorehearsal is so effective because it works directly at the level of
the learned function, and not indirectly on the representation of the
function within the network. We also briefly explore the effect of local
learning on generalisation within the task.