scholarly journals An olfactory pattern generator for on-demand combinatorial control of receptor activities

2021 ◽  
Author(s):  
Guangwei Si ◽  
Jacob Baron ◽  
Yu Feng ◽  
Aravinthan Samuel
Keyword(s):  
Olfactory Receptor ◽  
Mushroom Body ◽  
Receptive Fields ◽  
Olfactory Receptor Neuron ◽  
Pattern Generator ◽  
On Demand ◽  
Combinatorial Control ◽  
Spatially Distributed ◽  
Olfactory Systems ◽  
Drosophila Larva

Olfactory systems employ combinatorial receptor codes for odors. Systematically generating stimuli that address the combinatorial possibilities of an olfactory code poses unique challenges. Here, we present a stimulus method to probe the combinatorial code, demonstrated using the Drosophila larva. This method leverages a set of primary odorants, each of which targets the activity of one olfactory receptor neuron (ORN) type at an optimal concentration. Our setup uses microfluidics to mix any combination of primary odorants on demand to activate any desired combination of ORNs. We use this olfactory pattern generator to demonstrate a spatially distributed olfactory representation in the dendrites of a single interneuron in the antennal lobe, the first olfactory neuropil of the larva. In the larval mushroom body, the next processing layer, we characterize diverse receptive fields of a population of Kenyon cells. The precision and flexibility of the olfactory pattern generator will facilitate systematic studies of processing and transformation of the olfactory code.

SELECTIVITY IMPROVEMENT IN A MODEL OF OLFACTORY RECEPTOR NEURON WITH ADSORPTION-DESORPTION NOISE

2008 ◽  
Vol 16 (04) ◽  
pp. 531-545 ◽  
Author(s):  
A. K. VIDYBIDA ◽  
A. S. USENKO ◽  
J.-P. ROSPARS
Keyword(s):  
Olfactory Receptor ◽  
Model Neuron ◽  
Olfactory Receptor Neuron ◽  
Desorption Process ◽  
Receptor Proteins ◽  
Threshold Crossing ◽  
Olfactory Systems ◽  
Receptor Neurons ◽  
Adsorption Desorption

In biological olfactory systems, interaction of odorant molecules with olfactory receptor proteins is driven by Brownian motion. As a result, at chemical equilibrium, the total number of bound receptors changes randomly in time. Here we investigate the role of this effect, known in physics as adsorption-desorption noise, in the discriminating ability of olfactory receptor neurons. For this purpose we developed a computer program, which generates the adsorption-desorption process in a model neuron. We compared the processes resulting from two different odorants with different affinities for the receptor proteins. We took into account the threshold at which spikes are triggered and we calculated the neuronal selectivity due to the differences in the threshold-crossing statistics for the processes resulting from both odorants. We conclude that selectivity of the spiking response of the whole neuron is much greater than that of its receptor proteins in the near-threshold range of odorant concentrations.

scholarly journals Adaptive integrate-and-fire model reproduces the dynamics of olfactory receptor neuron responses in a moth

2019 ◽  
Vol 16 (157) ◽  
pp. 20190246 ◽  
Author(s):  
Marie Levakova ◽  
Lubomir Kostal ◽  
Christelle Monsempès ◽  
Philippe Lucas ◽  
Ryota Kobayashi
Keyword(s):  
Olfactory Receptor ◽  
Time Course ◽  
Olfactory Receptor Neuron ◽  
Receptor Activation ◽  
Olfactory Receptor Neurons ◽  
Response Dynamics ◽  
Spike Threshold ◽  
Olfactory Stimuli ◽  
Receptor Neurons ◽  
The Brain

In order to understand how olfactory stimuli are encoded and processed in the brain, it is important to build a computational model for olfactory receptor neurons (ORNs). Here, we present a simple and reliable mathematical model of a moth ORN generating spikes. The model incorporates a simplified description of the chemical kinetics leading to olfactory receptor activation and action potential generation. We show that an adaptive spike threshold regulated by prior spike history is an effective mechanism for reproducing the typical phasic–tonic time course of ORN responses. Our model reproduces the response dynamics of individual neurons to a fluctuating stimulus that approximates odorant fluctuations in nature. The parameters of the spike threshold are essential for reproducing the response heterogeneity in ORNs. The model provides a valuable tool for efficient simulations of olfactory circuits.

scholarly journals Olfactory receptor neuron coding in the turbulent realm

2013 ◽  
Vol 14 (S1) ◽  
Author(s):  
Jean-Baptiste Masson ◽  
Christelle Monsempes ◽  
Jean-Pierre Rospars ◽  
Philippe Lucas
Keyword(s):  
Olfactory Receptor ◽  
Olfactory Receptor Neuron ◽  
Receptor Neuron ◽  
Neuron Coding

scholarly journals Olfactory receptor neuron responses coding for rapid odour sampling

2011 ◽  
Vol 589 (9) ◽  
pp. 2261-2273 ◽  
Author(s):  
Ambarish S. Ghatpande ◽  
Johannes Reisert
Keyword(s):  
Olfactory Receptor ◽  
Olfactory Receptor Neuron ◽  
Receptor Neuron
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