Song, Z and Juusola, M (2014) Refractory sampling links efficiency and costs of sensory encoding to stimulus statistics. The Journal of Neuroscience, 34 (21). 7216 - 7237.
Abstract
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2014 Song and Juusola. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Drosophila; information theory; phototransduction; sampling; stochasticity; vision |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > School of Biosciences (Sheffield) > Department of Biomedical Science (Sheffield) |
Funding Information: | Funder Grant number BBSRC BB/F012071/1 BBSRC BB/D001900/1 BBSRC BB/H013849/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 18 Jun 2014 14:23 |
Last Modified: | 18 Jun 2014 14:23 |
Published Version: | http://dx.doi.org/10.1523/JNEUROSCI.4463-13.2014 |
Status: | Published |
Publisher: | Society for Neuroscience |
Refereed: | Yes |
Identification Number: | 10.1523/JNEUROSCI.4463-13.2014 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:79424 |