Information from the senses must be compressed into the limited range of responses that spiking neurons can generate. For optimal compression, the neuron's response should match the statistics of stimuli encountered in nature. Given a maximum firing rate, a nerve cell should learn to use each available firing rate equally often. Given a set mean firing rate, it should self-organize to respond with high firing rates only to comparatively rare events. Here we derive an unsupervised learning rule that continuously adapts membrane conductances of a Hodgkin-Huxley model neuron to optimize the representation of sensory information in the firing rate. Maximizing information transfer between the stimulus and the cell's firing rate can be interpreted as a non-Hebbian developmental mechanism.