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Computational Modelling of Early Olfactory Processing
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
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 [en]
olfaction, olfactory system, olfactory bulb, olfactory receptor neuron, synchronization, CaMKII, mathematical modelling
Keyword [sv]
lukt, luktsystemet, luktbulben, synkronisering, CaMKII, matematisk modellering
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-12090ISBN: 978-91-7415-575-4 (print)OAI: oai:DiVA.org:kth-12090DiVA: diva2:301250
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
List of papers
1. The impact of the distribution of isoforms on CaMKII activation
Open this publication in new window or tab >>The impact of the distribution of isoforms on CaMKII activation
2006 (English)In: Neurocomputing, ISSN 0925-2312, Vol. 69, no 10-12, 1010-1013 p.Article in journal (Refereed) Published
Abstract [en]

We have developed a computational model of the regulation of alpha- and beta-CaMKII activity, in order to examine (i) the importance of neighbour subunit interactions and (ii) the effect the higher CaMCa4 affinity of beta-CaMKII has on the holoenzyme activity in different configurations with the same alpha: beta ratio. The model consists of a deterministic biochemical network coupled to stochastic activation of CaMKII The results suggest that CaMKII holoenzyme activity is non-linear and dependent on the holoenzyme configuration of isoforms. This is especially pronounced in situations with a high-dephosphorylation rate of CaMKII.

Keyword
CaMKII; Plasticity; Computer modelling; Stochastic model
National Category
Information Science
Identifiers
urn:nbn:se:kth:diva-7227 (URN)10.1016/j.neucom.2005.12.035 (DOI)000237873900004 ()2-s2.0-33646520327 (Scopus ID)
Note
Hjorth, Johannes: Lic (Manuskript) QC 20100723Available from: 2007-05-30 Created: 2007-05-30 Last updated: 2012-01-08Bibliographically approved
2. Scaling effects in a model of the olfactory bulb
Open this publication in new window or tab >>Scaling effects in a model of the olfactory bulb
2007 (English)In: Neurocomputing, ISSN 0925-2312, Vol. 70, no 10-12, 1802-1807 p.Article in journal (Refereed) Published
Abstract [en]

Most computational models of the olfactory bulb are much smaller than any biological olfactory bulb-usually because the number of granule cells is much lower. The resulting subsampling of the inhibitory input may distort network dynamics and processing. We have constructed a large-scale model of the zebrafish olfactory bulb, as well as two smaller models, using the efficient parallellizing neural simulator SPLIT and data from a previously existing GENESIS model. We are studying several characteristics-among them overall behaviour, degree of synchrony of mitral cells and the timescale of appearance of synchrony-using cross-correlation plots and synthesized EEGs. Larger models with higher proportions of granule cells to mitral cells appear to give more synchronized output, especially for stimuli with shorter timescales.

Keyword
olfactory bulb; oscillation; synchronization
Identifiers
urn:nbn:se:kth:diva-7228 (URN)10.1016/j.neucom.2006.10.062 (DOI)000247215300040 ()2-s2.0-34247547342 (Scopus ID)
Note
QC20100723Available from: 2007-05-30 Created: 2007-05-30 Last updated: 2012-01-08Bibliographically approved
3. Modeling the response of a population of olfactory receptor neurons to an odorant
Open this publication in new window or tab >>Modeling the response of a population of olfactory receptor neurons to an odorant
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.

Keyword
Olfaction; Sensory coding; Olfactory receptor neuron; Neural population modeling
Identifiers
urn:nbn:se:kth:diva-12086 (URN)10.1007/s10827-009-0147-5 (DOI)000271105700003 ()2-s2.0-70350575367 (Scopus ID)
Note
QC20100723Available from: 2010-03-03 Created: 2010-03-03 Last updated: 2012-01-08Bibliographically approved
4. A Bulb Model Implementing Fuzzy Coding of Odor Concentration
Open this publication in new window or tab >>A Bulb Model Implementing Fuzzy Coding of Odor Concentration
2009 (English)In: Olfaction and Electronic Nose: Proceedings of the 13th International Symposium on Olfaction and Electronic Nose / [ed] Matteo Pardo, 2009, 159-162 p.Conference paper, (Refereed)
Abstract [en]

It is commonly accepted that the olfactory bulb (OB) codes for odor quality by specific patterns of activated glomeruli. However, no such consensus has been reached for how the OB codes for odor concentration. We have constructed a model of the olfactory bulb which is able to generate a "fuzzy code" for odor concentration, while still coding for odor identity and showing synchronization of active mitral cells. The fuzzy code arises from competitive inhibition in the glomerular layer of the model. Fuzzy concentration coding could explain how the OB might encode odor concentration while still encoding odor quality according to the consensus view above.

Series
AIP Conference Proceedings, ISSN 0094-243X ; 1137
Identifiers
urn:nbn:se:kth:diva-12087 (URN)10.1063/1.3156496 (DOI)000268929400035 ()2-s2.0-70450178318 (Scopus ID)978-0-7354-0674-2 (ISBN)
Conference
13th International Symposium on Olfaction and Electronic Nose
Note
QC20100723Available from: 2010-03-03 Created: 2010-03-03 Last updated: 2010-07-23Bibliographically approved
5. Varying Olfactory Receptor Numbers Give Rise to a Fuzzy Code for Stimulus Concentration.
Open this publication in new window or tab >>Varying Olfactory Receptor Numbers Give Rise to a Fuzzy Code for Stimulus Concentration.
(English)Manuscript (preprint) (Other academic)
Identifiers
urn:nbn:se:kth:diva-12088 (URN)
Note
QC20100723Available from: 2010-03-03 Created: 2010-03-03 Last updated: 2010-07-23Bibliographically approved

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