Keeping their distance? Odor response patterns along the concentration range

Strauch, Martin; Ditzen, Mathias; Galizia, C. Giovanni

doi:10.3389/fnsys.2012.00071

Cited by 19 publications

(17 citation statements)

References 48 publications

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“…This effect was specially marked among patterns of activity elicited by stimuli that differed in more than one order of magnitude in their concentrations ( figure 5D). Previous studies in honey bees have shown that increasing odor concentration increases the separation among patterns of activity elicited by different odors, and that generalization between different concentrations of the same odors is possible thanks to smooth transitions among the patterns elicited by different concentrations (Sachse & Galizia, 2003;Stopfer et al, 2003;Strauch et al, 2012). Furthermore, evidences from the present and previous works indicate that local computations based on the GABAergic network accomplish these two properties.…”

Section: Glomerular Odor Representationssupporting

confidence: 71%

“…However, it has been shown that honey bees discriminate odors better when odor concentration is high (Pelz et al, 1997;Wright & Smith, 2004). Consistent with this,Strauch et al, (Strauch et al, 2012) have shown that increasing odor concentration does not compromise pattern separation in the AL. These results indicate that gain modulation in the AL may not only favor pattern invariance across concentrations but also contribute to reduce the expected overlap when different odors increase their concentration.…”

Section: Gain Modulation Increases With the Number Of Active Glomerulimentioning

confidence: 85%

“…Gain control at the antennal lobe or the olfactory bulb is not absolute (this work, and (Cleland et al, 2007;Olsen et al, 2010;Sachse & Galizia, 2003;Storace & Cohen, 2017;Strauch et al, 2012;Zhu et al, 2013). The transformations that take place between input and output patterns attenuate the effect of the concentration but do not make it completely independent.…”

Section: Partial and Distributed Gain Controlmentioning

confidence: 94%

“…Previous studies have shown that computations in the AL optimizes the afferent input by enhancing the signal to noise contrast in odor representation, improving odor discrimination, and contributing to odor invariance across concentrations (Sachse & Galizia, 2003;Strauch, Ditzen, & Galizia, 2012). A critical element common to all these processes is the participation of local inhibitory neurons (Girardin et al, 2012;Sachse & Galizia, 2002;Stopfer et al, 1997).…”

Section: Gaba-a and Gaba-b Modulate Odor Elicited Activity In Pnsmentioning

confidence: 99%

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Gain modulation and odor concentration invariance in early olfactory networks

Marachlian

Huerta²,

Locatelli

2019

Preprint

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A conserved principle of the olfactory system, in most, if not all animals, is that each olfactory receptor interacts with different odorant molecules and each odorant molecule interacts with different olfactory receptors. This broad receptive field of the receptors constitutes the basis of a combinatorial code that allows animals to discriminate many more odorants than the actual number of receptor types that they express. A drawback is that high odorant concentrations recruit lower affinity receptors, which can give rise to the perception of qualitatively different odors. Here we addressed the contribution that early signal-processing in the honey bee antennal lobe does to keep odor representation stable across concentrations. We describe the contribution that GABA-A and GABA-B receptors-dependent-inhibition plays in terms of the amplitude and temporal profiles of the signals that convey odor information from the antennal lobes to the mushroom bodies. GABA reduces the amplitude of odor elicited signals and the number of glomeruli that are recruited in a concentration-dependent way. Blocking GABA-A and GABA-B receptors decreases the correlation among glomerular activity 1 patterns elicited by different concentrations of the same odor. Based on the results we built a realistic computational model of the antennal lobe that could be further used to evaluate the signal processing properties of the AL network under conditions that cannot be achieved in physiology experiments. Interestingly, even though based on rather simplistic topology and interactions among cells solely mediated by GABA-A and GABA-B interactions, the AL model reproduced the key features of the AL stable response in relation to different concentrations.

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Section: Glomerular Odor Representationssupporting

confidence: 71%

Section: Gain Modulation Increases With the Number Of Active Glomerulimentioning

confidence: 85%

Section: Partial and Distributed Gain Controlmentioning

confidence: 94%

Section: Gaba-a and Gaba-b Modulate Odor Elicited Activity In Pnsmentioning

confidence: 99%

See 2 more Smart Citations

Gain modulation and odor concentration invariance in early olfactory networks

Marachlian

Huerta²,

Locatelli

2019

Preprint

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“…We test our hypothesis in a detailed model of the honeybee AL, using a large data set from the literature (Ditzen, 2005;Strauch et al, 2012) to calibrate the responses to 16 odourants that we then use to make predictions for the responses to their synchronous and asynchronous mixtures.…”

Section: Introductionmentioning

confidence: 99%

Data-driven honeybee antennal lobe model suggests how stimulus-onset asynchrony can aid odour segregation

et al. 2013

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Data-driven honeybee antennal lobe model suggests how stimulus-onset asynchrony can aid odour segregation Article (Unspecified) http://sro.sussex.ac.uk Nowotny, Thomas, Stierle, Jacob S, Galizia, C Giovanni and Szyszka, Paul (2013) Data-driven honeybee antennal lobe model suggests how stimulus-onset asynchrony can aid odour segregation. Brain Research, 1536. pp. 119-134. ISSN 0006-8993 This version is available from Sussex Research Online: http://sro.sussex.ac.uk/47660/ This document is made available in accordance with publisher policies and may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the URL above for details on accessing the published version. Copyright and reuse:Sussex Research Online is a digital repository of the research output of the University.Copyright and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable, the material made available in SRO has been checked for eligibility before being made available.Copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. AbstractInsects have a remarkable ability to identify and track odour sources in multi-odour backgrounds. Recent behavioural experiments show that this ability relies on detecting millisecond stimulus asynchronies between odourants that originate from different sources. Honeybees, Apis mellifera, are able to distinguish mixtures where both odourants arrive at the same time (synchronous mixtures) from those where odourant onsets are staggered (asynchronous mixtures) down to an onset delay of only 6 ms. In this paper we explore this surprising ability in a model of the insects' primary olfactory brain area, the antennal lobe. We hypothesise that a winner-take-all inhibitory network of local neurons in the antennal lobe has a symmetrybreaking effect, such that the response pattern in projection neurons to an asynchronous mixture is different from the response pattern to the corresponding synchronous mixture for an extended period of time beyond the initial odourant onset where the two mixture conditions actually differ. The prolonged difference between response patterns to synchronous and asynchronous mixtures could facilitate odour segregation in downstream circuits of the olfactory pathway. We present a detailed data-driven model of the bee antennal lobe that reproduces a large data set of experimentally observed physiological odour responses, successfully implements the hypothesised symmetry-breaking mechanism and so demonstrates that this mechanism is con...

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Computational Modeling of Olfactory Behavior

Linster

Cleland

2022

Encyclopedia of Computational Neuroscience

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Keeping their distance? Odor response patterns along the concentration range

Cited by 19 publications

References 48 publications

Gain modulation and odor concentration invariance in early olfactory networks

Gain modulation and odor concentration invariance in early olfactory networks

Data-driven honeybee antennal lobe model suggests how stimulus-onset asynchrony can aid odour segregation

Computational Modeling of Olfactory Behavior

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