We argue that symmetry is an important consideration in addressing the problem
 of systematicity and investigate two forms of symmetry relevant to symbolic processes. 
 We implement this approach in terms of convolution and show that it can
 be used to achieve effective generalisation in three toy problems: rule learning,
 composition and grammar learning.
We use convolution to make neural networks behave more like symbolic systems.