Refractory sampling links efficiency and costs of sensory encoding to stimulus statistics.
Sensory neurons integrate information about the world, adapting their sampling to its changes. However, little is understood mechanistically how this primary encoding process, which ultimately limits perception, depends upon stimulus statistics. Here, we analyze this open question systematically by using intracellular recordings from fly (Drosophila melanogaster and Coenosia attenuata) photoreceptors and corresponding stochastic simulations from biophysically realistic photoreceptor models. Recordings show that photoreceptors can sample more information from naturalistic light intensity time series (NS) than from Gaussian white-noise (GWN), shuffled-NS or Gaussian-1/f stimuli; integrating larger responses with higher signal-to-noise ratio and encoding efficiency to large bursty contrast changes. Simulations reveal how a photoreceptor's information capture depends critically upon the stochastic refractoriness of its 30,000 sampling units (microvilli). In daylight, refractoriness sacrifices sensitivity to enhance intensity changes in neural image representations, with more and faster microvilli improving encoding. But for GWN and other stimuli, which lack longer dark contrasts of real-world intensity changes that reduce microvilli refractoriness, these performance gains are submaximal and energetically costly. These results provide mechanistic reasons why information sampling is more efficient for natural/naturalistic stimulation and novel insight into the operation, design, and evolution of signaling and code in sensory neurons.
Department of Biomedical Science, University of Sheffield, Sheffield S10 T2N, United Kingdom, and Vision Research Laboratory, Cognitive, Perceptual and Brain Sciences University College London, London, WC1E 6BT, United Kingdom.
Zhuoyi
Z
Song
National Key Laboratory of Cognitive Neuroscience and Learning, Beijing, Beijing Normal University, Beijing 100875, China, Department of Biomedical Science, University of Sheffield, Sheffield S10 T2N, United Kingdom, and m.juusola@sheffield.ac.uk.
Mikko
M
Juusola
D000255:Adenosine Triphosphate / Q000378:metabolism; D000818:Animals; D003116:Color; D003198:Computer Simulation; D004175:Diptera; D004307:Dose-Response Relationship, Radiation; D001330:Electronic Data Processing; D005260:Female; D008027:Light; D055537:Light Signal Transduction / Q000502:physiology*; D008297:Male; D008871:Microvilli / Q000502:physiology; D008954:Models, Biological; D010775:Photic Stimulation; D017956:Photoreceptor Cells, Invertebrate / Q000145:classification / Q000502:physiology*; D059629:Signal-To-Noise Ratio; D013997:Time Factors; D014796:Visual Perception / Q000502:physiology*
D000255:Adenosine Triphosphate
Drosophila; information theory; phototransduction; sampling; stochasticity; vision