Modeling the response of a population of olfactory receptor neurons to an odorant
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We modeled the firing rate of populations of olfactory receptor neurons (ORNs) responding to an odorant at different concentrations. Two cases were considered: a population of ORNs that all express the same olfactory receptor (OR), and a population that expresses many different ORs. To take into account ORN variability, we replaced single parameter values in a biophysical ORN model with values drawn from statistical distributions, chosen to correspond to experimental data. For ORNs expressing the same OR, we found that the distributions of firing frequencies are Gaussian at all concentrations, with larger mean and standard deviation at higher concentrations. For a population expressing different ORs, the distribution of firing frequencies can be described as the superposition of a Gaussian distribution and a lognormal distribution. Distributions of maximum value and dynamic range of spiking frequencies in the simulated ORN population were similar to experimental results.
KeywordsOlfaction Sensory coding Olfactory receptor neuron Neural population modeling
This work was supported by the European Network of Excellence “General Olfaction and Sensing Projects on a European Level” (GOSPEL) and by the European grant FP7 Bio-ICT convergence No. 216916 “Biologically inspired computation for chemical sensing” (Neurochem) to M.S., A.L., J.H.-K. and J.-P.R., and by the French-British grant ANR-BBSRC Sysbio 2007 “Pherosys” to J.-P.R. We thank Petr Lánský and two anonymous referees for helpful suggestions.
- Christensen, T. A., D’Alessandro, G., Lega, J., & Hildebrand, J. G. (2001). Morphometric modeling of olfactory circuits in the insect antennal lobe: I. Simulations of spiking local interneurons. BioSystems, 61, 143–153.Google Scholar
- Dougherty, D. P., Wright, G. A., & Yew, A. C. (2005). Computational model of the cAMP-mediated sensory response and calcium-dependent adaptation in vertebrate olfactory receptor neurons. Proceedings of the National Academy of Sciences of the United States of America, 102, 10415–10420. doi: 10.1073/pnas.0504099102.CrossRefPubMedGoogle Scholar
- Grosmaître, X., Vassalli, A., Mombaerts, P., Shepherd, G. M., & Ma, M. (2006). Odorant responses of olfactory sensory neurons expressing the odorant receptor MOR23: a patch clamp analysis in gene-targeted mice. Proceedings of the National Academy of Sciences of the United States of America, 103, 1970–1975. doi: 10.1073/pnas.0508491103.CrossRefPubMedGoogle Scholar
- Imanaka, Y., & Takeuchi, H. (2001). Spiking properties of olfactory receptor cells in the slice preparation. Chemical Senses, 26, 1023–1027.Google Scholar
- Martinez, D. (2005). Oscillatory synchronization requires precise and balanced feedback inhibition in a model of the insect antennal lobe. Neural Computation, 17, 2548–2570.Google Scholar
- Saltelli, A. (2004). Sensitivity analysis in practice: a guide to assessing scientific models. Wiley.Google Scholar
- Trotier, D. (1994). Intensity coding in olfactory receptor cells. Seminars in Cell Biology, 5, 47–54.Google Scholar
- Vermeulen, A., & Rospars, J.-P. (1998). Dendritic integration in olfactory sensory neurons: a steady-state analysis of how the neuron structure and neuron environment influence the coding of odor intensity. Journal of Computational Neuroscience, 5, 243–266. doi: 10.1023/A:1008826827728.Google Scholar