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

Abstract

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...

A Biophysical Model of the Early Olfactory System of Honeybees

“…The time series response to a single odour stimulus was generated using a set of ordinary differential equations describing the binding and activation of receptors as in [3]:…”

“…A typical modelling approach is to create reduced models using incomplete data. An example is the olfactory model in [3], which contains only the ORNs and corresponding glomeruli in the antennal lobe for which the data are available. However, it is unclear whether such models sufficiently relate to the biological systems they aim to describe, since the scaling of noise, synaptic efficacies and finite size network effects may change the dynamics of the system significantly.…”

“…However, it is unclear whether such models sufficiently relate to the biological systems they aim to describe, since the scaling of noise, synaptic efficacies and finite size network effects may change the dynamics of the system significantly. In addition, many numerical models create response patterns by directly fitting data from experiments in which specific properties of a subset of the systems are measured [3,4]. It cannot be expected that the predictions from these models would reflect other properties of the observed subsystem or any characteristics of the remainder of the system.…”

“…The resulting model comprises the full number of 160 different types of ORNs as well as local neurons (LNs) and projection neurons (PNs) organized in 160 corresponding glomeruli. The ORN response patterns are generated using a set of ordinary differential equations describing the binding and activation of receptors closely related to the actual biological processes [3,4], while the response of PNs are determined by network input from ORNs and LNs using a simple rate model derived from the leaky integrate-and-fire model.…”

“…To obtain the firing rate of the ORNs from its input conductances (which are assumed to be proportional to * ; see 2.2), the dynamics of a neuron is approximated by the conductance-based leaky integrate-and-fire model with adaptation as shown in (3).…”