Modelling the signal delivered by a population of first-order neurons in a moth olfactory system

Grémiaux, Alexandre; Nowotny, Thomas; Martinez, Dominique; Lucas, Philippe; Rospars, Jean-Pierre

doi:10.1016/j.brainres.2011.09.035

Cited by 21 publications

(14 citation statements)

References 32 publications

(42 reference statements)

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“…The rate of binding ( k 1 c ) n is not linear with respect to the stimulus concentration if the Hill coefficient n is unequal to one, reflecting the experimentally observed non-linearity of the odor transduction process [7,28]. To consider receptor activation for mixtures, this model needed to be extended to multiple components.…”

Section: Resultsmentioning

confidence: 99%

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Odorant mixtures elicit less variable and faster responses than pure odorants

et al. 2018

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In natural environments, odors are typically mixtures of several different chemical compounds. However, the implications of mixtures for odor processing have not been fully investigated. We have extended a standard olfactory receptor model to mixtures and found through its mathematical analysis that odorant-evoked activity patterns are more stable across concentrations and first-spike latencies of receptor neurons are shorter for mixtures than for pure odorants. Shorter first-spike latencies arise from the nonlinear dependence of binding rate on odorant concentration, commonly described by the Hill coefficient, while the more stable activity patterns result from the competition between different ligands for receptor sites. These results are consistent with observations from numerical simulations and physiological recordings in the olfactory system of insects. Our results suggest that mixtures allow faster and more reliable olfactory coding, which could be one of the reasons why animals often use mixtures in chemical signaling.

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Section: Resultsmentioning

confidence: 99%

“…Analysis of experimental data [28] shows that for most receptor types the Hill coefficient takes values between 0 and 1. This resonates well with the observation that responses to mixtures are predominantly hypoadditive/suppressive in data from previous experimental works [7,9,31].…”

Section: Resultsmentioning

confidence: 99%

Odorant mixtures elicit less variable and faster responses than pure odorants

et al. 2018

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“…The model is described in [69] with a few changes. Briefly, the response of each ORN stimulated at dose C is a spike train of latency L and interspike interval 1/ F ( Fig.…”

Section: Methodsmentioning

confidence: 99%

Heterogeneity and Convergence of Olfactory First-Order Neurons Account for the High Speed and Sensitivity of Second-Order Neurons

et al. 2014

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In the olfactory system of male moths, a specialized subset of neurons detects and processes the main component of the sex pheromone emitted by females. It is composed of several thousand first-order olfactory receptor neurons (ORNs), all expressing the same pheromone receptor, that contact synaptically a few tens of second-order projection neurons (PNs) within a single restricted brain area. The functional simplicity of this system makes it a favorable model for studying the factors that contribute to its exquisite sensitivity and speed. Sensory information—primarily the identity and intensity of the stimulus—is encoded as the firing rate of the action potentials, and possibly as the latency of the neuron response. We found that over all their dynamic range, PNs respond with a shorter latency and a higher firing rate than most ORNs. Modelling showed that the increased sensitivity of PNs can be explained by the ORN-to-PN convergent architecture alone, whereas their faster response also requires cell-to-cell heterogeneity of the ORN population. So, far from being detrimental to signal detection, the ORN heterogeneity is exploited by PNs, and results in two different schemes of population coding based either on the response of a few extreme neurons (latency) or on the average response of many (firing rate). Moreover, ORN-to-PN transformations are linear for latency and nonlinear for firing rate, suggesting that latency could be involved in concentration-invariant coding of the pheromone blend and that sensitivity at low concentrations is achieved at the expense of precise encoding at high concentrations.

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“…We assume here that ORNs expressing the same OR do not have a single value for the maximum conductance, but rather a distribution based on the profound differences in response kinetics as seen in the experimental studies (Rospars et al, 2003; Grosmaitre et al, 2006) and described by statistical population models (Sandström et al, 2009a; Grémiaux et al, 2012). Figure 2C shows the responses of two example receptor neurons to excitatory stimuli.…”

Section: Methodsmentioning

confidence: 99%

A spiking neural network model of self-organized pattern recognition in the early mammalian olfactory system

Kaplan

Lansner

2014

Front. Neural Circuits

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Olfactory sensory information passes through several processing stages before an odor percept emerges. The question how the olfactory system learns to create odor representations linking those different levels and how it learns to connect and discriminate between them is largely unresolved. We present a large-scale network model with single and multi-compartmental Hodgkin–Huxley type model neurons representing olfactory receptor neurons (ORNs) in the epithelium, periglomerular cells, mitral/tufted cells and granule cells in the olfactory bulb (OB), and three types of cortical cells in the piriform cortex (PC). Odor patterns are calculated based on affinities between ORNs and odor stimuli derived from physico-chemical descriptors of behaviorally relevant real-world odorants. The properties of ORNs were tuned to show saturated response curves with increasing concentration as seen in experiments. On the level of the OB we explored the possibility of using a fuzzy concentration interval code, which was implemented through dendro-dendritic inhibition leading to winner-take-all like dynamics between mitral/tufted cells belonging to the same glomerulus. The connectivity from mitral/tufted cells to PC neurons was self-organized from a mutual information measure and by using a competitive Hebbian–Bayesian learning algorithm based on the response patterns of mitral/tufted cells to different odors yielding a distributed feed-forward projection to the PC. The PC was implemented as a modular attractor network with a recurrent connectivity that was likewise organized through Hebbian–Bayesian learning. We demonstrate the functionality of the model in a one-sniff-learning and recognition task on a set of 50 odorants. Furthermore, we study its robustness against noise on the receptor level and its ability to perform concentration invariant odor recognition. Moreover, we investigate the pattern completion capabilities of the system and rivalry dynamics for odor mixtures.

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Modelling the signal delivered by a population of first-order neurons in a moth olfactory system

Cited by 21 publications

References 32 publications

Odorant mixtures elicit less variable and faster responses than pure odorants

Odorant mixtures elicit less variable and faster responses than pure odorants

Heterogeneity and Convergence of Olfactory First-Order Neurons Account for the High Speed and Sensitivity of Second-Order Neurons

A spiking neural network model of self-organized pattern recognition in the early mammalian olfactory system

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