title: A biomimetic fly photoreceptor model elucidates how stochastic adaptive quantal sampling provides a large dynamic range.
abstract: Light intensities (photons s-1μm-2) 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 into its limited output range, so that a signal is neither buried in noise in darkness nor saturated in brightness. A fly photoreceptor has achieved such a large dynamic range; it can encode intensity changes from single to billions of photons, outperforming man-made light sensors. This performance requires powerful light adaptation, the neural implementation of which has only become clear 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 signalling pathway to adaptively transduce photons into quantum bumps (QBs, or samples). QBs then sum the macroscopic photoreceptor responses, governed by four quantal sampling factors (limitations): (i) the number of photon sampling units in the cell structure (microvilli), (ii) sample size (QB waveform), (iii) latency distribution (time delay between photon arrival and emergence of a QB), and (iv) 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.
authors.affiliation: Department of Biomedical Science, University of Sheffield, Sheffield, S10 2TN, UK.
authors.forename: Zhuoyi
authors.identifier: 0000-0001-9991-4053
authors.initials: Z
authors.lastname: Song
authors.affiliation: Department of Biomedical Science, University of Sheffield, Sheffield, S10 2TN, UK.|State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
authors.forename: Mikko
authors.identifier: 0000-0002-4428-5330
authors.initials: M
authors.lastname: Juusola
mesh_terms: D000818:Animals; D032701:Biomimetics; D004175:Diptera / Q000502:physiology*; D008027:Light; D008871:Microvilli / Q000502:physiology; D017785:Photons; D017956:Photoreceptor Cells, Invertebrate / Q000502:physiology* / Q000648:ultrastructure; D013269:Stochastic Processes
keywords: fly photoreceptor; gain control; large dynamic range; light adaptation; phototransduction; quantum sampling; stochastic adaptive sampling