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  • 1.
    Sandström, Malin
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Computational Modelling of Early Olfactory Processing2010Doctoral 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.

  • 2.
    Sandström, Malin
    KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.
    Early Information Processing in the Vertebrate Olfactory System: A Computational Study2007Licentiate thesis, comprehensive summary (Other scientific)
    Abstract [en]

    The olfactory system is believed to be the oldest sensory system. It developed to detect and analyse chemical information in the form of odours, and its organisation 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 has 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, both experimental and theoretical. 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 the basis of a biologically realistic model of the olfactory system. Our goal is to be able to represent the whole olfactory system. We are not there yet, but we have some of the necessary building blocks; a model of the input from the olfactory receptor neuron population and a model of the olfactory bulb. Taking into account the reported variability of geometrical, electrical and receptor-dependent neuronal characteristics, we have been able to model the frequency response of a population of olfactory receptor neurons. By constructing several olfactory bulb models of different size, we have shown that the size of the bulb network has an impact on its ability to process noisy information. We have also, through biochemical modelling, investigated the behaviour of the enzyme CaMKII which is known to be critical for early olfactory adaptation (suppression of constant odour stimuli).

  • 3.
    Sandström, Malin
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Hellgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Scaling effects in a model of the olfactory bulb2007In: Neurocomputing, ISSN 0925-2312, Vol. 70, no 10-12, 1802-1807 p.Article in journal (Refereed)
    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.

  • 4.
    Sandström, Malin
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Hjorth, Johannes
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Hellgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    The impact of the distribution of isoforms on CaMKII activation2006In: Neurocomputing, ISSN 0925-2312, Vol. 69, no 10-12, 1010-1013 p.Article in journal (Refereed)
    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.

  • 5.
    Sandström, Malin
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Källgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Varying Olfactory Receptor Numbers Give Rise to a Fuzzy Code for Stimulus Concentration.Manuscript (preprint) (Other academic)
  • 6.
    Sandström, Malin
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Hellgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Rospars, Jean-Pierre
    Modelling the population of olfactory receptor neurons2007Manuscript (preprint) (Other academic)
  • 7.
    Sandström, Malin
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Hellgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Rospars, Jean-Pierre
    Modeling the response of a population of olfactory receptor neurons to an odorant2009In: Journal of Computational Neuroscience, ISSN 0929-5313, E-ISSN 1573-6873, Vol. 27, 337-355 p.Article in journal (Refereed)
    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.

  • 8.
    Sandström, Malin
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Proschinger, Thomas
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    A Bulb Model Implementing Fuzzy Coding of Odor Concentration2009In: 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.

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