The 'retroaxonal hypothesis' (Harris, 2008) posits a role for slow retrograde signalling in learning. It is based on the intuition that cells with strong output synapses tend to be those that encode useful information; and that cells which encode useful information should not modify their input synapses too readily. The hypothesis has two parts: rst, that the stronger a cell's output synapses, the less likely it is to change its input synapses; and second, that a cell is more likely to revert changes to its input synapses when the changes are followed by weakening of its output synapses. It is motivated in part by analogy between a neural network and a market economy, viewing neurons as 'entrepreneurs' who 'sell' spike trains to each other. In this view, the slow retrograde signals which tell a neuron that it has strong output synapses are 'money' and imply that what it produces is useful. This thesis constructs a mathematical model of learning, which validates the intuition of the retroaxonal hypothesis. In this model, we show that neurons can estimate their usefulness, or 'worth', from the magnitude of their output weights. We also show that by making each cell's input synapses more or less plastic according to its worth, the performance of a network can be improved.