The glomerular code for odor representation is species specific in the honeybee Apis mellifera

Galizia, C. Giovanni; Sachse, Silke; Rappert, Angelika; Menzel, Randolf

doi:10.1038/8144

Cited by 297 publications

(311 citation statements)

References 30 publications

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“…While the model ORN responses are highly correlated as in the corresponding experimental ORN data, the model PN responses are mostly uncorrelated, with the peak of the probability around zero correlation. Our model results for both ORNs and PNs show a good fit to their experimental counterparts in [1,13] qualitatively, even though the model was only fitted to ORN data. Please note that the 'model responses' correspond to firing rate, which may not map directly to Ca 2+ imaging data.…”

Section: Response To Continuous Stimulimentioning

confidence: 64%

“…Asymptotic responses of 28 ORNs for time-invariant odor inputs at high concentration to 16 different odors have been measured using calcium imaging of glomeruli with bath-applied Ca 2+ dyes [1]. We adopted these responses directly to form the responses of the first 28 ORNs in our model.…”

Section: Asymptotic Response Of Model Orns For Time-invariant Odor Inmentioning

confidence: 99%

“…We then generated the responses of the remaining 132 ORNs to the same 16 odors using a method inspired by [6]. The response patterns were generated from a combination of previously generated responses, including those from [1], and noise. The parameters were chosen such that the statistical distribution of the pairwise correlations of ORNs across odours in the generated responses matches that of the 28 ORNs adopted from data.…”

Section: Asymptotic Response Of Model Orns For Time-invariant Odor Inmentioning

confidence: 99%

“…Unfortunately, due to limitations in experimental techniques, these data are necessarily noisy and incomplete. For example, in honeybees, responses of only around 30 of a total of 160 known types of olfactory receptor neurons (ORNs) can routinely be measured [1,2]. A typical modelling approach is to create reduced models using incomplete data.…”

Section: Introductionmentioning

confidence: 99%

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A Biophysical Model of the Early Olfactory System of Honeybees

Chan

Nowotny

2017

Neural Information Processing

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Abstract. Experimental measurements often can only provide limited data from an animal's sensory system. In addition, they exhibit large trial-to-trial and animal-to-animal variability. These limitations pose challenges to building mathematical models intended to make biologically relevant predictions. Here, we present a mathematical model of the early olfactory system of honeybees aiming to overcome these limitations. The model generates olfactory response patterns which conform to the statistics derived from experimental data for a variety of their properties. This allows considering the full dimensionality of the sensory input space as well as avoiding overfitting the underlying data sets. Several known biological mechanisms, including processes of chemical binding and activation of receptors, and spike generation and transmission in the antennal lobe network, are incorporated in the model at a minimal level. It can therefore be used to study how experimentally observed phenomena are shaped by these underlying biophysical processes. We verified that our model can replicate some key experimental findings that were not used when building it. Given appropriate data, our model can be generalized to the early olfactory systems of other insects. It hence provides a possible framework for future numerical and analytical studies of olfactory processing in insects.

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Section: Response To Continuous Stimulimentioning

confidence: 64%

Section: Asymptotic Response Of Model Orns For Time-invariant Odor Inmentioning

confidence: 99%

Section: Asymptotic Response Of Model Orns For Time-invariant Odor Inmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Biophysical Model of the Early Olfactory System of Honeybees

Chan

Nowotny

2017

Neural Information Processing

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“…This genetically encoded architecture induces a stimulus-dependent spatial code in the glomeruli [55,56,57,23,58]. Moreover, the spatial code is maintained across individuals of the same species [59] as would be expected given the genetic structure. In principle this peripheral olfactory structure already seems to be able to discriminate among odors at this early stage.…”

Section: Early Codementioning

confidence: 89%

Learning pattern recognition and decision making in the insect brain

Huerta¹

2013

AIP Conference Proceedings

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Abstract. We revise the current model of learning pattern recognition in the Mushroom Bodies of the insects using current experimental knowledge about the location of learning, olfactory coding and connectivity. We show that it is possible to have an efficient pattern recognition device based on the architecture of the Mushroom Bodies, sparse code, mutual inhibition and Hebbian leaning only in the connections from the Kenyon cells to the output neurons. We also show that despite the conventional wisdom that believes that artificial neural networks are the bioinspired model of the brain, the Mushroom Bodies actually resemble very closely Support Vector Machines (SVMs). The derived SVM learning rules are situated in the Mushroom Bodies, are nearly identical to standard Hebbian rules, and require inhibition in the output. A very particular prediction of the model is that random elimination of the Kenyon cells in the Mushroom Bodies do not impair the ability to recognize odorants previously learned.

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Modular representations of odorants in the glomerular layer of the rat olfactory bulb and the effects of stimulus concentration

2000

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To study the mechanism whereby odorants are encoded in the nervous system, we studied the glomerular-layer activity patterns in the rat olfactory bulb evoked by closely related odorants from different chemical families. These odorants had a common straight-chain hydrocarbon structure, but differed systematically in their functional groups. Neural activity was mapped across the entire glomerular layer by using the ¿(14)C2-deoxyglucose method. Group responses were averaged and compared by using data matrices. The glomerular activity patterns that resulted from this analysis were comprised of modules. Unique combinations of modules were activated by each odorant, demonstrating what may be part of the neural code for odorants. Most of the modules were clustered together in the bulb, perhaps providing for enhanced contrast between related chemicals by means of lateral inhibition. We also determined whether changes in odorant concentration would affect spatial patterns of glomerular activity. Two odorants, pentanal and 2-hexanone, evoked different patterns at increased concentrations, with additional glomeruli being recruited at a great distance from glomeruli in which activity was evoked at lower concentrations. Humans report that both of these odorants change in perceived odor with increasing concentration. Three other odorants (pentanoic acid, methyl pentanoate, and pentanol) did not recruit new areas of glomerular activation with increasing concentration, and humans do not report a changed odor across concentrations of these odorants. The results suggest that changes in modular glomerular activity patterns could underlie altered odor perception across odorant concentrations, and they provide additional support for a combinatorial, spatially based code in the olfactory system.

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The glomerular code for odor representation is species specific in the honeybee Apis mellifera

Cited by 297 publications

References 30 publications

A Biophysical Model of the Early Olfactory System of Honeybees

A Biophysical Model of the Early Olfactory System of Honeybees

Learning pattern recognition and decision making in the insect brain

Modular representations of odorants in the glomerular layer of the rat olfactory bulb and the effects of stimulus concentration

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