  The problem of optimising a network of discretely firing neurons is
addressed. An objective function is introduced which measures the average
number of bits that are needed for the network to encode its state. When this
is minimised, it is shown that this leads to a number of results, such as
topographic mappings, piecewise linear dependence on the input of the
probability of a neuron firing, and factorial encoder networks.
