A molecular odorant transduction model and the complexity of spatio-temporal encoding in the Drosophila antenna

Lazar, Aurel A.; Yeh, Chung-Heng

doi:10.1371/journal.pcbi.1007751

Cited by 11 publications

(87 citation statements)

References 51 publications

(97 reference statements)

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“…series of input pulses which durations and inter-stimulus-intervals were drawn from a Gaussian distribution. We found that our model reproduces the data to a similar quality (relative error of around 6%) as previous linear-nonlinear models (Kim et al, 2011(Kim et al, , 2015Martelli et al, 2013;Nagel and Wilson, 2011;Lazar and Yeh, 2020), even though it has fewer free parameters (see Figure 3).…”

Section: Constraining the Orn Model To Biophysical Evidencesupporting

confidence: 71%

“…in line with previous experimental and theoretical studies (Kim et al, 2011(Kim et al, , 2015Martelli et al, 2013;Lazar and Yeh, 2020). We simulated pairs of ORNs expressing different OR types, as they are co-housed in sensilla.…”

Section: Resultsmentioning

confidence: 77%

“…Because of our hypotheses that the role of NSIs is to improve processing of temporally complex stimuli, we focused on a description which included temporal dynamics but was otherwise as simple as possible. We therefore have simplified 1. the cellular dynamics of odor transduction (Kaissling, 2001(Kaissling, , 2009(Kaissling, , 2014(Kaissling, , 2019Gu and Rospars, 2011;Gorur-Shandilya et al, 2017) and only heuristically describe the macroscopic effects at the receptor neuron level, an approach similar to (Lazar and Yeh, 2020); 2. the complexity of the full receptor repertoire in the insect olfactory system, e.g. about 60 ORN types in Drosophila, and instead focused on a single sensillum with two co-housed ORNs; 3. the true complexity of the many different LN types and transmitters in the AL (Silbering et al, 2008), using only GABA A -like LNs.…”

Section: The Model and Its Limitationsmentioning

confidence: 99%

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Non-synaptic interactions between olfactory receptor neurons, a possible key feature of odor processing in flies

Pannunzi¹,

Nowotny²

2020

Preprint

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When flies explore their environment, they encounter odors in complex, highly intermittent plumes. To navigate a plume and, for example, find food, flies must solve several tasks, including reliably identifying mixtures of odorants and discriminating odorant mixtures emanating from a single source from odorants emitted from separate sources and mixing in the air. Lateral inhibition in the antennal lobe is commonly understood to help solving these two tasks. With a computational model of the Drosophila olfactory system, we analyze the utility of an alternative mechanism for solving them: Non-synaptic (“ephaptic”) interactions (NSIs) between olfactory receptor neurons that are stereotypically co-housed in the same sensilla. For both tasks, NSIs improve the insect olfactory system and outperform the standard lateral inhibition mechanism in the antennal lobe. These results shed light, from an evolutionary perspective, on the role of NSIs, which are normally avoided between neurons, for instance by myelination.

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Section: Constraining the Orn Model To Biophysical Evidencesupporting

confidence: 71%

Section: Resultsmentioning

confidence: 77%

Section: The Model and Its Limitationsmentioning

confidence: 99%

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Non-synaptic interactions between olfactory receptor neurons, a possible key feature of odor processing in flies

Pannunzi¹,

Nowotny²

2020

Preprint

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“…• Changing the affinity values of each of the odorant-receptor pairs characterizing the input of the Odorant Transduction Process [37],…”

Section: Eoscircuits Library For Larva and Adult Early Olfactory Circmentioning

confidence: 99%

“…• Changing parameter values of the Biological Spike Generators (BSGs) associated with each OSN [37],…”

Section: Eoscircuits Library For Larva and Adult Early Olfactory Circmentioning

confidence: 99%

FlyBrainLab: Accelerating the Discovery of the Functional Logic of theDrosophilaBrain in the Connectomic/Synaptomic Era

Lazar¹,

Liu²,

Türkcan³

et al. 2020

Preprint

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In recent years, a wealth of Drosophila neuroscience data have become available. These include cell type, connectome and synaptome datasets for both the larva and adult fly. To facilitate integration across data modalities and to accelerate the understanding of the functional logic of the fly brain, we developed an interactive computing environment called FlyBrainLab.FlyBrainLab is uniquely positioned towards accelerating the discovery of the functional logic of the Drosophila brain. Its interactive open source architecture seamlessly integrates and brings together computational models with neuroanatomical, neurogenetic and electrophysiological data, changing the organization of neuroscientific fly brain data from a group of unconnected databases, arrays and tables, to a well structured data and executable circuit repository. The FlyBrainLab User Interface supports a highly intuitive and automated workflow that streamlines the 3D exploration and visualization of fly brain circuits, and the interactive exploration of the functional logic of executable circuits created directly from the explored and visualized fly brain data.FlyBrainLab methodologically supports the efficient comparison of fly brain circuit models, across model instances developed by different researchers, across different developmental stages of the fruit fly and across different datasets. The FlyBrainLab Utility and Circuit Libraries accelerate the creation of models of executable circuits. The Utility Libraries help untangle the graph structure of neural circuits from raw connectome and synaptome data. The Circuit Libraries facilitate the exploration of neural circuits of the neuropils of the central complex and, the development and implementation of models of the adult and larva fruit fly early olfactory systems.To elevate its executable circuit construction capability beyond the connectome, FlyBrainLab provides additional libraries for molecular transduction arising in sensory coding in vision and olfaction. Together with sensory neuron activity data, these libraries serve as entry points for discovering circuit function in the sensory systems of the fruit fly brain. They also enable the biological validation of developed executable circuits within the same platform.

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Adaptive temporal processing of odor stimuli

2021

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The olfactory system translates chemical signals into neuronal signals that inform behavioral decisions of the animal. Odors are cues for source identity, but if monitored long enough, they can also be used to localize the source. Odor representations should therefore be robust to changing conditions and flexible in order to drive an appropriate behavior. In this review, we aim at discussing the main computations that allow robust and flexible encoding of odor information in the olfactory neural pathway.

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A molecular odorant transduction model and the complexity of spatio-temporal encoding in the Drosophila antenna

Cited by 11 publications

References 51 publications

Non-synaptic interactions between olfactory receptor neurons, a possible key feature of odor processing in flies

Non-synaptic interactions between olfactory receptor neurons, a possible key feature of odor processing in flies

FlyBrainLab: Accelerating the Discovery of the Functional Logic of theDrosophilaBrain in the Connectomic/Synaptomic Era

Adaptive temporal processing of odor stimuli

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