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

Marie Levakova,Lubomir Kostal,Ryota Kobayashi,Philippe Lucas,Christelle Monsempès

doi:10.1098/rsif.2019.0246

Abstract

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.

Highlights

Many animals rely on olfaction for detecting food, natural predators and mating partners
We investigated whether the leaky integrateand-fire (LIF) model with receptor dynamics (2.1)–(2.5) can reproduce the average response of olfactory receptor neurons (ORNs) to a pheromone pulse stimulus
We present a computational model of a moth ORN that reproduces the firing rate dynamics of an ORN under intermittent pheromone stimulation over a 1000-fold range of concentrations

Summary

Introduction

Many animals rely on olfaction for detecting food, natural predators and mating partners. The odorant is initially recognized by olfactory receptor neurons (ORNs). The information is transferred to a secondary region, either the antennal lobe in insects or olfactory bulb in vertebrates. Projections from the secondary region extend to higher order brain regions, the mushroom body and lateral horn in insects and the orbitofrontal cortex, amygdala, entorhinal cortex and ventral striatum in vertebrates. The architecture of the olfactory circuit differs from that of other sensory modalities (for a review, see [1,2]); for example, the olfactory circuit consists of fewer layers. Concepts derived from experimental and theoretical studies on other systems may not be applicable to olfaction. Computational models that can replicate the behaviour of real ORNs during odorant stimulation may generate testable hypotheses on mechanisms underlying olfactory transduction and encoding

Methods

Results

Conclusion