Olfactory Coding in the Insect Pheromone Pathway: Models and Experiments
Lead Research Organisation:
University of Sussex
Department Name: Sch of Engineering and Informatics
Abstract
'Much can be learned about complex systems by studying simpler ones. [...] Small systems, and particularly small olfactory systems, seem to use mechanisms and strategies that are not unique to them and we may be better off starting with the modest goal of understanding flies first.' G. Laurent 'Shall we even understand the fly brain?' in 23 Problems in Systems Neuroscience, Oxford University Press, 2006. Our understanding of the computations that take place in the human brain is limited by the extreme complexity of the cortex, and by the difficulty of experimentally recording neural activities, for practical and ethical reasons. Just as the Human Genome Project was preceded by the sequencing of smaller but complete genomes, it is likely that future breakthroughs in neuroscience will result from the study of smaller but complete nervous systems, such as the insect brain. These small nervous systems exhibit general properties that are also present in higher mammals, such as neural synchronization and network oscillations, and we are more likely to understand the role of these phenomena in insects first, before we can apply this knowledge to humans. This project analyzes olfaction, the sense of smell, and uses the moth olfactory brain as a model, because (i) it is relatively simple, (ii) it has been widely described and (iii) it is easily accessible to electrophysiological recordings. Our aim is to understand how sensory information is coded and processed during the detection and processing of odour stimuli, with special emphasis on communication by sexual pheromones. Pheromonal communication constitutes an exceptionally favourable model system for studying olfactory mechanisms, because (i) it is specialized and oriented (emitting females, receiving males), (ii) it involves only a small number of known and available ligands (sexual pheromones) which interact with specific membrane receptors, (iii) the cerebral neural network processing pheromonal information is well delimited and specialized, and (iv) the behavioural response is well characterized. The moth olfactory brain is made of three sets of neurons: (a) the olfactory receptor neurons (ORNs) of the antenna, in large numbers, detect and code the quality (nature of molecules), intensity (number of molecules) and temporal characteristics of the pheromonal signal; (b) the neurons, especially the projection neurons (PNs) of the antennal lobe (AL) in the brain, in smaller numbers, integrate the information delivered by ORNs. All synaptic connections, between ORNs, AL neurons and modulatory neurons from other parts of the brain, take place in a set of ca. 60 glomeruli. In particular, a subset of 2-3 enlarged, sexually dimorphic glomeruli, the macroglomerular complex (MGC), processes the pheromonal information. (c) The Kenyon cells of the mushroom bodies (MBs), in large numbers, process the information received from the PNs. This neural system is of significant scientific and socio-economic interest. Although long neglected, the study of olfaction has considerably expanded over the last fifteen years, stimulated by the interest in its molecular and neural mechanisms as well as the potential applications in many areas. Among the latter are the control of insect populations and 'artificial noses' (a rapidly expanding area of considerable economic importance). The research proposed here will investigate the principles underlying the superior performance of biological olfactory systems and thus will provide the basis for novel developments in these fields.
Technical Summary
The aim of our project is to investigate olfactory information processing in the first stages of the olfactory pathway. We will conduct experiments and modelling studies in the pheromone subsystem of the moth Spodoptera littoralis (the cotton leafworm). 1. In experiments we will a) investigate the transduction mechanism in the olfactory receptor neurons (ORNs) with tip-recording in vivo and patch clamp recordings in vitro, b) determine the prevalence and distribution of different types of ORNs and their projections to the macro-glomerular complex (MGC), and c) record the activity of MGC neurons in response to a rich set of odour stimuli while monitoring the local field potential. 2. In modelling we will a) build detailed, biophysical models of the ORNs, combining experimental data (1b) with existing submodels of perireception, reception, and post-reception processes, b) reconstruct the compound signal sent by the ORNs to the MGC based on modelling (2a) and experimental data (1b), c) develop conductance-based models of single glomeruli using modern data fitting technology (synchronization based parameter estimation employing genetic algorithms and multiple shooting) to adjust the models to the data (1b, 1c), d) reduce the detailed models to network models of IF neurons and analyse their response and coding properties mathematically, and e) build similar detailed and reduced models of the full MGC to analyse the following open questions (in both the one-glomerulus and full MGC models): i) Is the connectivity of olfactory systems well described by a random network or does it follow a different construction principle? ii) How does the pheromone system achieve its outstanding sensitivity and specificity and maintain them in the presence of noise? iii) What is the origin and role of oscillations in the MGC or olfactory systems in general? What is the function of phase locking and synchronization and how does inhibition contribute to it?
Publications
Nowotny Thomas
(2011)
BIO-MIMETIC CLASSIFICATION ON MODERN PARALLEL HARDWARE: REALIZATIONS ON NVIDIAG® CUDA™
in INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL
Diamond A
(2015)
Comparing Neuromorphic Solutions in Action: Implementing a Bio-Inspired Solution to a Benchmark Classification Task on Three Parallel-Computing Platforms.
in Frontiers in neuroscience
Zavada A
(2011)
Competition-based model of pheromone component ratio detection in the moth.
in PloS one
Nowotny T
(2009)
Divergence alone cannot guarantee stable sparse activity patterns if connections are dense
in BMC Neuroscience
Huerta R
(2009)
Fast and robust learning by reinforcement signals: explorations in the insect brain.
in Neural computation
Nowotny T
(2009)
Homeostasis versus neuronal variability: Models and experiments in crustaceans
in Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology
Belmabrouk H
(2011)
Interaction of cellular and network mechanisms for efficient pheromone coding in moths.
in Proceedings of the National Academy of Sciences of the United States of America
Nowotny T
(2013)
Machine learning for automatic prediction of the quality of electrophysiological recordings.
in PloS one
Grémiaux A
(2012)
Modelling the signal delivered by a population of first-order neurons in a moth olfactory system.
in Brain research
Buckley C
(2009)
Moving beyond convergence in the pheromone system of the moth
in BMC Neuroscience
Description | see final report submitted on Je-S |
Exploitation Route | Our models and experimental results have been fully published in prominent places and will be used in future research. The cnrun simulator is open source and public and has been integrated into a Debian Linux distribution. |
Sectors | Agriculture Food and Drink Environment |
URL | http://www.informatics.sussex.ac.uk/research/projects/PheroSys/index.php/Main_Page |
Description | CSIRO Flagship Collaboration Fund |
Amount | $51,679 (AUD) |
Organisation | Commonwealth Scientific and Industrial Research Organisation |
Sector | Public |
Country | Australia |
Start | 04/2013 |
End | 04/2014 |
Description | HFSP Program Grant |
Amount | $337,500 (USD) |
Funding ID | RGP0053/2015 |
Organisation | Human Frontier Science Program (HFSP) |
Sector | Charity/Non Profit |
Country | France |
Start | 06/2015 |
End | 06/2018 |
Description | OCE Distinguished Visiting Scientist Award |
Amount | $15,000 (AUD) |
Organisation | Commonwealth Scientific and Industrial Research Organisation |
Sector | Public |
Country | Australia |
Start | 06/2011 |
End | 03/2013 |
Title | Model of Pheromone Ratio Recognition |
Description | A conductance based neuronal network model of the Macro-Glomerular complex of moths aimed at explaining how moths recognise pheromone blends regardless of concentration. |
Type Of Material | Computer model/algorithm |
Year Produced | 2011 |
Provided To Others? | Yes |
Impact | this is difficult to uncover. |
URL | http://senselab.med.yale.edu/modeldb/ShowModel.asp?model=143753 |
Title | Moth MGC Model |
Description | A Moth MGC Model-A HH network with quantitative rate reduction. It consists of a network of Hodgkin Huxley neurons coupled by slow GABA_B synapses which is run alongside a quantitative reduction described in the associated paper. |
Type Of Material | Computer model/algorithm |
Year Produced | 2011 |
Provided To Others? | Yes |
Impact | this is hard to uncover. |
URL | http://senselab.med.yale.edu/modeldb/ShowModel.asp?model=144403 |
Description | Collaboration with INRA Versailles and LORIA Nancy in the PheroSys project |
Organisation | French National Institute of Agricultural Research |
Department | INRA Versailles |
Country | France |
Sector | Academic/University |
PI Contribution | Several co-authored publications as detailed in the publication list. |
Collaborator Contribution | see above. |
Impact | See grant outocmes. |
Start Year | 2008 |
Description | Collaboration with INRA Versailles and LORIA Nancy in the PheroSys project |
Organisation | National Center for Scientific Research (Centre National de la Recherche Scientifique CNRS) |
Department | Lorraine Research Laboratory in Computer Science and its Applications (LORIA) |
Country | France |
Sector | Public |
PI Contribution | Several co-authored publications as detailed in the publication list. |
Collaborator Contribution | see above. |
Impact | See grant outocmes. |
Start Year | 2008 |
Title | cnrun - a neural network simulator that simulates neural networks provided as NeuroML specifications |
Description | With this software users can simulate neural networks that they describe in the NeuroML markup language. This description typically would be derived from a separate tool such as NeuroConstruct. |
Type Of Technology | Software |
Year Produced | 2011 |
Open Source License? | Yes |
Impact | This software has been integrated in a Debian Release. |
URL | http://sourceforge.net/projects/cnrun/ |
Description | Extending the critical brain hypothesis to the rate domain: A case study of the pheromone system of the moth |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Participants in your research or patient groups |
Results and Impact | Invited oral presentation on the 9th International Workshop Neural Coding, Limassol 2010. no actual impacts realised to date |
Year(s) Of Engagement Activity | 2010 |
URL | http://www.cs.ucy.ac.cy/nc2010/ |
Description | Maximal Dynamic Range in Inhibitory Neuronal Networks Close to Bifurcation |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | regional |
Primary Audience | Participants in your research or patient groups |
Results and Impact | Poster presentation at the ICMS Mathematical Neuroscience Workshop, Edinburgh 2011. no actual impacts realised to date |
Year(s) Of Engagement Activity | 2011 |
URL | http://soundcloud.com/mercyuk/21-september-podcast ( |
Description | Neural coding in the olfactory system of insects |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Participants in your research or patient groups |
Results and Impact | Invited oral presentation at the 9th International Workshop Neural Coding, Limassol 2010. no actual impacts realised to date |
Year(s) Of Engagement Activity | 2010 |