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A photoreceptor's information capture is constrained by the structure and function of its light-sensitive parts. Specifically, in a fly photoreceptor, this limit is set by the number of its photon sampling units (microvilli), constituting its light sensor (the rhabdomere), and the speed and recoverability of their phototransduction reactions. In this review, using an insightful constructionist viewpoint of a fly photoreceptor being an ‘imperfect’ photon counting machine, we explain how these constraints give rise to adaptive quantal information sampling in time, which maximises information in responses to salient light changes while antialiasing visual signals. Interestingly, such sampling innately determines also why photoreceptors extract more information, and more economically, from naturalistic light contrast changes than Gaussian white-noise stimuli, and we explicate why this is so. Our main message is that stochasticity in quantal information sampling is less noise and more processing, representing an ‘evolutionary adaptation’ to generate a reliable neural estimate of the variable world.Different steps for estimating a fly photoreceptor's rate of information transfer to naturalistic light intensity time series. A, naturalistic light intensity patterns can be collected from natural surroundings or from natural images. These can further include estimated modulation by a fly's normal saccadic visual behaviours. B, naturalistic light stimulation can be played back to a fly photoreceptor by a calibrated LED stimulus system during intracellular recording, or it can be used as light input to a biophysically realistic photoreceptor model. C, voltage responses to repeated stimulus presentations can be recorded or simulated. D, information sampling dynamics in the recordings and simulations are estimated by using mathematic methods of Shannon's information theory.