This response heterogeneity of ORNs might be caused, for example, by differences in the density of olfactory receptors (ORs), odorant-binding proteins and odorant-degrading enzymes among ORNs. Responses of different ORNs to the same pheromone pulse exhibited marked cell-to-cell variability ( figure 1 b) as reported in previous studies. The shaded area represents the range between the lower and upper quartile trajectory. ( c– f) The average firing rate across cells in response to the same 0.5 s pulse stimulus of pheromone at different doses (1–1000 pg). Note the heterogeneity in firing rates between the two ORNs despite stimulation by the same pheromone pulse. Bottom: Raster plots of 10 trials (rows) from each cell. Top: The average firing rate of each cell. ( b) Examples of spike trains generated by two ORNs (cells A and B) in response to 0.5 s of constant pheromone stimulation at 100 pg. ( a) ORNs were stimulated by intermittent delivery of the sex pheromone (four pheromone doses ranging from 1 to 1000 pg) to mimic fluctuating odorant concentration in a pheromone plume. The mathematical tractability and simplicity of the proposed model allows for efficient simulations and analysis of ORN spiking activity.Įxperimental data for the responses of olfactory receptor neurons (ORNs) to pheromone stimulation. We demonstrate that an adaptation mechanism in spike threshold is necessary to reproduce the response dynamics of ORNs. Here, we develop a computational model for individual ORNs that generates spikes in response to dynamic odorant stimulation. However, the LIF model cannot accurately replicate the response dynamics. A few models incorporating receptor activation into a simple spike generation mechanism based on the LIF model have been developed in order to study steady-state ORN behaviour.
Reduced neuronal models, such as the leaky integrate-and-fire (LIF) neuron, can be good approximations of real neurons and therefore useful tools for simulating and investigating prominent features of network dynamics. To understand the mechanisms of pheromone detection, it is essential to develop a computational model that replicates odorant-evoked ORN responses. Receptor activation and related downstream signalling cascades leading to membrane depolarization have been described by various mathematical models, including detailed biophysical models. Odorant molecules are first absorbed by the sensillum lymph, where they initiate a cascade of complex biochemical interactions. Pheromone detection in moth ORNs occurs in two stages: receptor activation by the odorant and action potential (spike) generation. Such models have also been used to clarify the coding properties of ORNs such as the stimulus–response relationship of the ORNs and the implications of the efficient coding hypothesis.
Indeed, computational models have enhanced our understanding of the mechanisms underlying odorant detection in both invertebrates and vertebrates and facilitated investigations of olfactory pathway functions. Computational models that can replicate the behaviour of real ORNs during odorant stimulation may generate testable hypotheses on mechanisms underlying olfactory transduction and encoding.
Therefore concepts derived from experimental and theoretical studies on other systems may not be applicable to olfaction. The architecture of the olfactory circuit differs from that of other sensory modalities (for a review, see ) for example, the olfactory circuit consists of fewer layers. 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 information is then transferred to a secondary region, either the antennal lobe in insects or olfactory bulb in vertebrates. The odorant is initially recognized by olfactory receptor neurons (ORNs).
Many animals rely on olfaction for detecting food, natural predators and mating partners.
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