  Adaptation in the retina is thought to optimize the encoding of natural light
signals into sequences of spikes sent to the brain. However, adaptation also
entails computational costs: adaptive code is intrinsically ambiguous, because
output symbols cannot be trivially mapped back to the stimuli without the
knowledge of the adaptive state of the encoding neuron. It is thus important to
learn which statistical changes in the input do, and which do not, invoke
adaptive responses, and ask about the reasons for potential limits to
adaptation. We measured the ganglion cell responses in the tiger salamander
retina to controlled changes in the second (contrast), third (skew) and fourth
(kurtosis) moments of the light intensity distribution of spatially uniform
temporally independent stimuli. The skew and kurtosis of the stimuli were
chosen to cover the range observed in natural scenes. We quantified adaptation
in ganglion cells by studying two-dimensional linear-nonlinear models that
capture well the retinal encoding properties across all stimuli. We found that
the retinal ganglion cells adapt to contrast, but exhibit remarkably invariant
behavior to changes in higher-order statistics. Finally, by theoretically
analyzing optimal coding in LN-type models, we showed that the neural code can
maintain a high information rate without dynamic adaptation despite changes in
stimulus skew and kurtosis.
