  We tested the hypothesis that the neural code of retinal ganglion cells is
optimized to transmit visual information at minimal metabolic cost. Under a
broad ensemble of light patterns, ganglion cell spike trains consisted of
sparse, precise bursts of spikes. These bursts were viewed as independent
neural symbols. The noise in each burst was measured via repeated presentation
of the visual stimulus, and the energy cost was estimated from the total charge
flow in a biophysically realistic model of ganglion cell spiking. Given these
costs and noise, the theory of efficient codes predicts an optimal distribution
of symbol usage. Symbols that are either noisy or metabolically costly are
suppressed in this optimal code. We found excellent qualitative and
quantitative agreement with the measured distribution of burst sizes for
ganglion cells in the tiger salamander retina.
