  Sensory systems across all modalities and species exhibit adaptation to
continuously changing input statistics. Individual neurons have been shown to
modulate their response gains so as to maximize information transmission in
different stimulus contexts. Experimental measurements have revealed
additional, nuanced sensory adaptation effects including changes in response
maxima and minima, tuning curve repulsion from the adapter stimulus, and
stimulus-driven response decorrelation. Existing explanations of these
phenomena rely on changes in inter-neuronal synaptic efficacy, which, while
more flexible, are unlikely to operate as rapidly or reversibly as single
neuron gain modulations. Using published V1 population adaptation data, we show
that propagation of single neuron gain changes in a recurrent network is
sufficient to capture the entire set of observed adaptation effects. We propose
a novel adaptive efficient coding objective with which single neuron gains are
modulated, maximizing the fidelity of the stimulus representation while
minimizing overall activity in the network. From this objective, we
analytically derive a set of gains that optimize the trade-off between
preserving information about the stimulus and conserving metabolic resources.
Our model generalizes well-established concepts of single neuron adaptive gain
control to recurrent populations, and parsimoniously explains experimental
adaptation data.
