A biomimetic fly photoreceptor model elucidates how stochastic adaptive quantal sampling provides a large dynamic range

Light intensities (photons/s/um2) in a natural scene vary over several orders of magnitude from shady woods to direct sunlight. A major challenge facing the visual system is how to map such a large dynamic input range to its limited output range, so that signal is neither buried into noise in darkness and nor saturated in brightness. A fly photoreceptor has achieved such a large dynamic range; it can encode intensity changes from single photons to billions more, outperforming man-made light sensors. This performance requires powerful light-adaptation, the neural implementation of which has only become clearer recently. A computational fly photoreceptor model, which mimics the real phototransduction processes, has elucidated how light adaptation happens dynamically through stochastic adaptive quantal information sampling. A Drosophila R1-R6 photoreceptor’s light-sensor, the rhabdomere, has 30,000 microvilli, each of which stochastically samples incoming photons. Each microvillus employs a full G-protein-coupled-receptor (GPCR) signalling pathway to adaptively transduce photons into quantum bumps (QBs, or samples). QBs then sum up the macroscopic photoreceptor responses, governed by four quantal sampling factors (limitations): (1) the number of photon sampling units in the cell structure (microvilli); (2) sample size (QB waveform); (3) latency distribution (time delay between photon arrival to emergence of a QB), and (4) refractory period distribution (time for a microvillus to recover after a QB). Here, we review how these factors jointly orchestrate light adaptation over a large dynamic range.