2D encoding of concentration and concentration gradient in Drosophila ORNs
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1
Columbia University, United States
The lack of a deeper understanding of how olfactory receptor neurons (ORNs) encode odors has hindered progress in understanding olfactory signal processing in higher brain centers. We investigate the encoding of time-varying odor stimuli by Drosophila ORNs and their spike domain representation for further processing by the network of glomeruli. We built a novel low-turbulence odor delivery system that enables a precise control and reproducible delivery of airborne odorants. The system provides exact control of odor concentration and concentration gradient on a millisecond time scale. Using a photo-ionization detector for monitoring odor concentration values in real-time, we found that odor waveforms reaching the antennae of fruit flies can be reproduced to within a tolerance of 1%. We augmented the odor delivery system with the capability of simultaneously recording the in-vivo extracellular activity of two ORNs. A wide range of time-varying odor waveforms were used in in-vivo recordings of ORNs expressing the same receptor. Spiking activity of single ORNs activated by essentially the same odor waveforms could be evaluated from repeated experiments for a wide range of concentration and concentration gradient value pairs. In order to evaluate the spike-timing precision, we simultaneously recorded from a single fly the activity of two neurons expressing the same receptor. Overall, we recorded the spiking activity of (i) neurons expressing different receptors in response to the same odorant, and (ii) neurons expressing the same receptor in response to different odorants. Our analysis of the recordings demonstrates that ORNs respond to a given time-varying stimulus with a high degree of spike-timing precision. This precision is conserved across multiple repetitions of the same time-varying odor waveform for ORNs expressing the same receptor. Further, we report a qualitatively stereotyped response to a given waveform across a population of excited ORNs in a single fly and across different flies. Based on an extensive analysis of the recordings, we propose a novel two-dimensional encoding paradigm for the representation of excitatory time-varying odor stimuli by fly ORNs. We identify odor concentration and its rate of change as the predominant odor characteristics determining the response of ORNs. Using concentration and concentration gradient as input variables, we construct a novel 2D encoding manifold in a three-dimensional space that characterizes the response of a neuron. We quantitatively show how to predict the response of an ORN to an excitatory time-varying stimulus by choosing an appropriate trajectory embedded in the 2D encoding manifold. Our work demonstrates an adaptive two-dimensional encoding mechanism for Drosophila ORNs. At very low odorant concentrations, ORNs encode positive concentration gradients. Conversely, at high concentrations ORNs encode the odorant concentration. The 2D encoding manifold clearly shows that Drosophila ORNs encode both odor concentration and concentration gradient and provides a quantitative description of the neural response with a predictive power not seen before. Acknowledgements. The work presented here was supported by NIH under grant number R01DC008701-01 and was conducted in the Axel laboratory at Columbia University. The authors would like to thank Dr. Richard Axel for insightful discussions and his outstanding support.
Conference:
Computational and Systems Neuroscience 2010, Salt Lake City, UT, United States, 25 Feb - 2 Mar, 2010.
Presentation Type:
Poster Presentation
Topic:
Poster session III
Citation:
Kim
AJ,
Lazar
AA and
Slutskiy
Y
(2010). 2D encoding of concentration and concentration gradient in Drosophila ORNs.
Front. Neurosci.
Conference Abstract:
Computational and Systems Neuroscience 2010.
doi: 10.3389/conf.fnins.2010.03.00280
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Received:
05 Mar 2010;
Published Online:
05 Mar 2010.
*
Correspondence:
Anmo J Kim, Columbia University, New York, United States, aurel@ee.columbia.edu