title: Fly Photoreceptors Encode Phase Congruency.
abstract: More than five decades ago it was postulated that sensory neurons detect and selectively enhance behaviourally relevant features of natural signals. Although we now know that sensory neurons are tuned to efficiently encode natural stimuli, until now it was not clear what statistical features of the stimuli they encode and how. Here we reverse-engineer the neural code of Drosophila photoreceptors and show for the first time that photoreceptors exploit nonlinear dynamics to selectively enhance and encode phase-related features of temporal stimuli, such as local phase congruency, which are invariant to changes in illumination and contrast. We demonstrate that to mitigate for the inherent sensitivity to noise of the local phase congruency measure, the nonlinear coding mechanisms of the fly photoreceptors are tuned to suppress random phase signals, which explains why photoreceptor responses to naturalistic stimuli are significantly different from their responses to white noise stimuli.
authors.affiliation: Department of Automatic Control & Systems Engineering, the University of Sheffield, Mappin Street, Sheffield, S1 3JD, United Kingdom.
authors.forename: Uwe
authors.initials: U
authors.lastname: Friederich
authors.affiliation: Department of Automatic Control & Systems Engineering, the University of Sheffield, Mappin Street, Sheffield, S1 3JD, United Kingdom.
authors.forename: Stephen A
authors.initials: SA
authors.lastname: Billings
authors.affiliation: Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge, CB2 3DY, United Kingdom.
authors.forename: Roger C
authors.initials: RC
authors.lastname: Hardie
authors.affiliation: Department of Biomedical Science, the University of Sheffield, Western Bank, Sheffield, S10 2TN, United Kingdom.
authors.forename: Mikko
authors.initials: M
authors.lastname: Juusola
authors.affiliation: Department of Automatic Control & Systems Engineering, the University of Sheffield, Mappin Street, Sheffield, S1 3JD, United Kingdom.
authors.forename: Daniel
authors.initials: D
authors.lastname: Coca
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D004330:Drosophila; D055724:Electrophysiological Phenomena; D008962:Models, Theoretical; D010775:Photic Stimulation; D017956:Photoreceptor Cells, Invertebrate / Q000502:physiology*