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Modeling the response of a population of olfactory receptor neurons to an odorant
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.ORCID iD: 0000-0002-2358-7815
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.ORCID iD: 0000-0002-0550-0739
2009 (English)In: Journal of Computational Neuroscience, ISSN 0929-5313, E-ISSN 1573-6873, Vol. 27, 337-355 p.Article in journal (Refereed) Published
Abstract [en]

We modeled the firing rate of populations of olfactory receptor neurons (ORNs) responding to an odorant at different concentrations. Two cases were considered: a population of ORNs that all express the same olfactory receptor (OR), and a population that expresses many different ORs. To take into account ORN variability, we replaced single parameter values in a biophysical ORN model with values drawn from statistical distributions, chosen to correspond to experimental data. For ORNs expressing the same OR, we found that the distributions of firing frequencies are Gaussian at all concentrations, with larger mean and standard deviation at higher concentrations. For a population expressing different ORs, the distribution of firing frequencies can be described as the superposition of a Gaussian distribution and a lognormal distribution. Distributions of maximum value and dynamic range of spiking frequencies in the simulated ORN population were similar to experimental results.

Place, publisher, year, edition, pages
2009. Vol. 27, 337-355 p.
Keyword [en]
Olfaction; Sensory coding; Olfactory receptor neuron; Neural population modeling
Identifiers
URN: urn:nbn:se:kth:diva-12086DOI: 10.1007/s10827-009-0147-5ISI: 000271105700003Scopus ID: 2-s2.0-70350575367OAI: oai:DiVA.org:kth-12086DiVA: diva2:301240
Note
QC20100723Available from: 2010-03-03 Created: 2010-03-03 Last updated: 2012-01-08Bibliographically approved
In thesis
1. Computational Modelling of Early Olfactory Processing
Open this publication in new window or tab >>Computational Modelling of Early Olfactory Processing
2010 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Chemical sensing is believed to be the oldest sensory ability. The chemical senses, olfaction and gustation, developed to detect and analyze information in the form of air- or waterborne chemicals, to find food and mates, and to avoid danger. The organization of the olfactory system follows the same principles in almost all living animals, insects as well as mammals. Likely, the similarities are due to parallel evolution – the same type of organisation seems to have arisen more than once. Therefore, the olfactory system is often assumed to be close to optimally designed for its tasks.Paradoxically, the workings of the olfactory system are not yet well known,although several milestone discoveries have been made during the last decades. The most well-known is probably the disovery of the olfactory receptor gene family,announced in 1991 by Linda Buck and Richard Axel. For this and subsequent work, they were awarded a Nobel Prize Award in 2004. This achievement has been of immense value for both experimentalists and theorists, and forms the basis of the current understanding of olfaction. The olfactory system has long been a focus for scientific interest within several fields, both experimental and theoretical, and it has often been used asa model system. And ever since the field of computational neuroscience was founded, the functions of the olfactory system have been investigated through computational modelling. In this thesis, I present several approaches to biologically realistic computational models of parts of the olfactory system, with an emphasis on the earlier stages of the vertebrate olfactory system – olfactory receptor neurons (ORNs) and the olfactory bulb (OB). I have investigated the behaviour of the enzyme CaMKII, which is known to be critical for olfactory adaptation (suppression of constant odour stimuli) in the ORN, using a biochemical model. By constructing several OB models of different size, I have shown that the size of the OB network has an impact on its ability to process noisy information. Taking into account the reported variability of geometrical, electrical and receptor-dependent neuronal characteristics, I have been able to model the frequency response of a population of ORNs. I have used this model to find the key properties that govern most of the ORN population’s response, and investigated some of the possible implications of these key properties in subsequent studies of the ORN population and the OB – what we call the fuzzy concentration coding hypothesis.

Abstract [sv]

Detektion av kemiska ämnen anses allmänt vara den äldsta sensoriska förmågan. De kemiska sinnena, lukt och smak, utvecklades för att upptäcka och analysera kemisk information i form av luft- eller vattenburna ämnen, för att hitta mat och partners, och för att undvika fara. Luktsystemet är organiserat efter samma principer hos nästan alla djurarter, insekter såväl som däggdjur. Troligen beror likheterna på parallell evolution – samma organisation verkar ha uppstått mer än en gång. Därför antas det ofta att luktsystemet är nära optimalt anpassat för sina arbetsuppgifter.Paradoxalt nog är luktsystemets arbetsprinciper ännu inte väl kända, även om flera banbrytande framsteg gjorts de senaste decennierna. Det mest välkända är nog upptäckten av genfamiljen av luktreceptorer, som tillkännagavs 1991 av Linda Buck och Rikard Axel. För detta och efterföljande arbete belönades de med Nobelpriset år 2004. Upptäckten har varit mycket värdefull för både experimentalister och teoretiker, och är grunden för vår nuvarande förståelse av luktsystemet. Luktsystemet har länge varit ett fokus för vetenskapligt intresse inom flera fält, experimentella såväl som teoretiska, och har ofta använts som ett modellsystem. Och ända sedan fältet beräkningsneurobiologi grundades har luktsystemet undersökts genom datormodellering. I denna avhandling presenterar jag flera ansatser till biologiskt realistiskaberäkningsmodeller av luktsystemet, med tonvikt på de tidigare delarna av ryggradsdjurens luktsystem – luktreceptorceller och luktbulben. Jag har undersökt beteendet hos enzymet CaMKII, som anses vara kritiskt viktigt för adaptation (undertryckning av ständigt närvarande luktstimuli) i luktsystemet, i en biokemisk modell. Genom att konstruera flera olika stora modeller av luktbulben har jag visat att storleken på luktbulbens cellnätverk påverkar dess förmåga att behandla brusig information. Genom att ta hänsyn till nervcellernas rapporterade variationer i geometriska, elektriska och receptor-beroende karaktärsdrag har jag lyckats modellera svarsfrekvenserna från en population av luktreceptorceller. Jag har använt denna modell för att hitta de nyckelprinciper som styr huvuddelen av luktreceptorneuron-populationens svar, ochundersökt några av de tänkbara konsekvenserna av dessa nyckelprinciper i efterföljande studier av luktreceptorneuron-populationen och luktbulben – det vi kallar ”fuzzy concentration coding”-hypotesen.

Place, publisher, year, edition, pages
Stockholm: KTH, 2010. xiv, 151 p.
Series
Trita-CSC-A, ISSN 1653-5723 ; 2010:03
Keyword
olfaction, olfactory system, olfactory bulb, olfactory receptor neuron, synchronization, CaMKII, mathematical modelling, lukt, luktsystemet, luktbulben, synkronisering, CaMKII, matematisk modellering
National Category
Computer Science
Identifiers
urn:nbn:se:kth:diva-12090 (URN)978-91-7415-575-4 (ISBN)
Public defence
2010-03-26, D3, Lindstedtsvägen 5, Stockholm, 13:00 (English)
Opponent
Supervisors
Note
QC20100723Available from: 2010-03-04 Created: 2010-03-03 Last updated: 2010-07-23Bibliographically approved

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