Literature DB >> 23743263

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

Thomas Nowotny1, Jacob S Stierle, C Giovanni Galizia, Paul Szyszka.   

Abstract

Insects 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 6ms. In this paper we explore this surprising ability in a model of the insects' primary olfactory brain area, the antennal lobe. We hypothesize that a winner-take-all inhibitory network of local neurons in the antennal lobe has a symmetry-breaking 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 odoursegregation 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 consistent with our current knowledge of the olfactory circuits in the bee brain. This article is part of a Special Issue entitled Neural Coding 2012.
Copyright © 2013 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Computational model; Mixture; Odour object recognition; Odoursegregation; Olfactory system; Stimulus asynchrony

Mesh:

Year:  2013        PMID: 23743263     DOI: 10.1016/j.brainres.2013.05.038

Source DB:  PubMed          Journal:  Brain Res        ISSN: 0006-8993            Impact factor:   3.252


  7 in total

1.  High-speed odor transduction and pulse tracking by insect olfactory receptor neurons.

Authors:  Paul Szyszka; Richard C Gerkin; C Giovanni Galizia; Brian H Smith
Journal:  Proc Natl Acad Sci U S A       Date:  2014-11-10       Impact factor: 11.205

2.  Rapid and slow chemical synaptic interactions of cholinergic projection neurons and GABAergic local interneurons in the insect antennal lobe.

Authors:  Ben Warren; Peter Kloppenburg
Journal:  J Neurosci       Date:  2014-09-24       Impact factor: 6.167

3.  Olfactory learning without the mushroom bodies: Spiking neural network models of the honeybee lateral antennal lobe tract reveal its capacities in odour memory tasks of varied complexities.

Authors:  HaDi MaBouDi; Hideaki Shimazaki; Martin Giurfa; Lars Chittka
Journal:  PLoS Comput Biol       Date:  2017-06-22       Impact factor: 4.475

4.  Odorant mixtures elicit less variable and faster responses than pure odorants.

Authors:  Ho Ka Chan; Fabian Hersperger; Emiliano Marachlian; Brian H Smith; Fernando Locatelli; Paul Szyszka; Thomas Nowotny
Journal:  PLoS Comput Biol       Date:  2018-12-10       Impact factor: 4.475

5.  Olfactory Object Recognition Based on Fine-Scale Stimulus Timing in Drosophila.

Authors:  Aarti Sehdev; Yunusa G Mohammed; Tilman Triphan; Paul Szyszka
Journal:  iScience       Date:  2019-02-18

Review 6.  Olfactory coding in honeybees.

Authors:  Marco Paoli; Giovanni C Galizia
Journal:  Cell Tissue Res       Date:  2021-01-14       Impact factor: 5.249

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

Authors:  Mario Pannunzi; Thomas Nowotny
Journal:  PLoS Comput Biol       Date:  2021-12-13       Impact factor: 4.475

  7 in total

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