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• The model was used for calculations in the following paper: "Selectivity Gain in Olfactory Receptor Neuron at Optimal Odor Concentration", in: 2024 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) :1-3 (2024), DOI: 10.1109/ISOEN61239.2024.10556323. PDF,
  
  Description of the model:
  It has been observed experimentally that at low odor concentration an olfactory receptor neuron (ORN) is subjected to highly fluctuating stimulation by the odor molecules. See, e.g.: Menini,A., Picco,C., Firestein,S. Quantal-like current fluctuations induced by odorants in olfactory receptor cells, Nature 373(6513):435-437 (1995) https://doi.org/10.1038/373435a0
  It was shown theoretically for an extremely simplified model of ORN that, due to fluctuations, ORN's selectivity at low odor concentrations can be considerably better then that of its receptor proteins (OR). See, e.g.: A.Vidybida. "Harnessing thermal fluctuations for selectivity gain," 2022 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN), Aveiro, Portugal, 2022, pp. 1-3, doi: 10.1109/ISOEN54820.2022.9789678, available also here.
  
  This model of stochastic odor reception is designed to check this same effect of selectivity gain for a more realistic ORN model. As a model we use the leaky integrate-and-fire (LIF) one populated with receptor proteins treated as ligand-gated ion channels. Preliminary estimates of the selectivity gain obtained for this model can be seen here.
  
  The ORN here is modeled as a LIF neuron with an excitable membrane characterized by a relaxation constant, resting voltage, firing threshold voltage, total surface, the surface density of OR proteins taken from published experimental data. With each bound OR we associate a single open channel with a concrete conductance and reversal voltage. If an ORN is exposed to an odor, the total number n of OR proteins bound with odor molecules changes randomly due to stochastic nature of the binding-releasing process, and similarly do the transmembrane currents. In this model, the binding-releasing process is simulated as a Markov stochastic process and the selectivity gain is calculated as the program output.
  
  Experimental motivation: It is widely considered that the selectivity of ORN and that of its receptor proteins (OR) are the same thing. On the other hand, by comparing experimental data for the former (expressed in terms of the spike rate) and the latter (expressed in terms of the number of open channels, the calcium responses) it can be concluded that the ORN's selectivity can be better than that of its OR's. See, e.g. Fig. 1(a) from: Galizia,C.G., Münch,D., Strauch,M., Nissler,A., Ma,Shouwen. "Integrating Heterogeneous Odor Response Data into a Common Response Model: A DoOR to the Complete Olfactome", Chemical Senses 35(7):551-563 (2010), https://doi.org/10.1093/chemse/bjq042 . One possible reason for the selectivity gain could be the stochastic nature of the process of binding-releasing of odor molecules by the ORs. This model is developed to check such a possibility.
  
  Model Type: Neuron or other electrically excitable cell
  
  Cell Type(s): Abstract integrate-and-fire leaky neuron with ligand gated ion channels
  
  Receptors: Olfactory Receptors, Ligand gated ion channels
  
  Model Concept(s): Sensory coding; Sensory processing; Simplified Models; Stimulus selectivity; Stochastic stimulus reception
  
  Simulation Environment: C or C++ program
  
  
  
  Keywords: olfaction, ORN, selectivity, receptor proteins, fluctuations, stochastic process, Markov process, LIF neuron
  
  The model's code with detailed description is available here with password: "Read-only access code" without quote marks, or here.