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Map formation in the olfactory bulb by axon guidance of olfactory neurons
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. (Stockholm Brain Institute, Stockholm, Sweden)
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. (Stockholm Brain Institute, Stockholm, Sweden)ORCID iD: 0000-0002-1213-4239
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. (Stockholm Brain Institute, Stockholm, Sweden; Stockholm University, Stockholm, Sweden)
2011 (English)In: Frontiers in Systems Neuroscience, ISSN 1662-5137, Vol. 5, no 0Article in journal (Refereed) Published
Abstract [en]

The organization of representations in the brain has been observed to locally reflect subspaces of inputs that are relevant to behavioral or perceptual feature combinations, such as in areas receptive to lower and higher-order features in the visual system. The early olfactory system developed highly plastic mechanisms and convergent evidence indicates that projections from primary neurons converge onto the glomerular level of the olfactory bulb (OB) to form a code composed of continuous spatial zones that are differentially active for particular physico?-chemical feature combinations, some of which are known to trigger behavioral responses. In a model study of the early human olfactory system, we derive a glomerular organization based on a set of real-world,biologically-relevant stimuli, a distribution of receptors that respond each to a set of odorants of similar ranges of molecular properties, and a mechanism of axon guidance based on activity. Apart from demonstrating activity-dependent glomeruli formation and reproducing the relationship of glomerular recruitment with concentration, it is shown that glomerular responses reflect similarities of human odor category perceptions and that further, a spatial code provides a better correlation than a distributed population code. These results are consistent with evidence of functional compartmentalization in the OB and could suggest a function for the bulb in encoding of perceptual dimensions.

Place, publisher, year, edition, pages
Ch. de la Pécholettaz 6 CH – 1066 Epalinges Switzerland: Frontiers Media SA , 2011. Vol. 5, no 0
Keyword [en]
olfaction, plasticity, axonal guidance, olfactory coding, olfactory bulb, glomeruli, odor category, perception
National Category
Bioinformatics (Computational Biology) Neurosciences
URN: urn:nbn:se:kth:diva-45308DOI: 10.3389/fnsys.2011.00084ScopusID: 2-s2.0-84856170519OAI: diva2:452231
This Document is Protected by copyright and was first published by Frontiers. All rights reserved. It is reproduced with permission.Available from: 2011-10-31 Created: 2011-10-28 Last updated: 2012-02-24Bibliographically approved
In thesis
1. Machine Learning Techniques with Specific Application to the Early Olfactory System
Open this publication in new window or tab >>Machine Learning Techniques with Specific Application to the Early Olfactory System
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis deals with machine learning techniques for the extraction of structure and the analysis of the vertebrate olfactory pathway based on related methods. Some of its main contributions are summarized below.

We have performed a systematic investigation for classification in biomedical images with the goal of recognizing a material in these images by its texture. This investigation included (i) different measures for evaluating the importance of image descriptors (features), (ii) methods to select a feature set based on these evaluations, and (iii) classification algorithms. Image features were evaluated according to their estimated relevance for the classification task and their redundancy with other features. For this purpose, we proposed a framework for relevance and redundancy measures and, within this framework, we proposed two new measures. These were the value difference metric and the fit criterion. Both measures performed well in comparison with other previously used ones for evaluating features. We also proposed a Hopfield network as a method for feature selection, which in experiments gave one of the best results relative to other previously used approaches.

We proposed a genetic algorithm for clustering and tested it on several realworld datasets. This genetic algorithm was novel in several ways, including (i) the use of intra-cluster distance as additional optimization criterion, (ii) an annealing procedure, and (iii) adaptation of mutation rates. As opposed to many conventional clustering algorithms, our optimization framework allowed us to use different cluster validation measures including those which do not rely on cluster centroids. We demonstrated the use of the clustering algorithm experimentally with several cluster validity measures as optimization criteria. We compared the performance of our clustering algorithm to that of the often-used fuzzy c-means algorithm on several standard machine learning datasets from the University of California/Urvine (UCI) and obtained good results.

The organization of representations in the brain has been observed at several stages of processing to spatially decompose input from the environment into features that are somehow relevant from a behavioral or perceptual standpoint. For the perception of smells, the analysis of such an organization, however, is not as straightforward because of the missing metric. Some studies report spatial clusters for several combinations of physico-chemical properties in the olfactory bulb at the level of the glomeruli. We performed a systematic study of representations based on a dataset of activity-related images comprising more than 350 odorants and covering the whole spatial array of the first synaptic level in the olfactory system. We found clustered representations for several physico-chemical properties. We compared the relevance of these properties to activations and estimated the size of the coding zones. The results confirmed and extended previous studies on olfactory coding for physico-chemical properties. Particularly of interest was the spatial progression by carbon chain that we found. We discussed our estimates of relevance and coding size in the context of processing strategies. We think that the results obtained in this study could guide the search into olfactory coding primitives and the understanding of the stimulus space.

In a second study on representations in the olfactory bulb, we grouped odorants together by perceptual categories, such as floral and fruity. By the application of the same statistical methods as in the previous study, we found clustered zones for these categories. Furthermore, we found that distances between spatial representations were related to perceptual differences in humans as reported in the literature. This was possibly the first time that such an analysis had been done. Apart from pointing towards a spatial decomposition by perceptual dimensions, results indicate that distance relationships between representations could be perceptually meaningful.

In a third study, we modeled axon convergence from olfactory receptor neurons to the olfactory bulb. Sensory neurons were stimulated by a set of biologically-relevant odors, which were described by a set of physico-chemical properties that covaried with the neural and glomerular population activity in the olfactory bulb. Convergence was mediated by the covariance between olfactory neurons. In our model, we could replicate the formation of glomeruli and concentration coding as reported in the literature, and further, we found that the spatial relationships between representational zones resulting from our model correlated with reported perceptual differences between odor categories. This shows that natural statistics, including similarity of physico-chemical structure of odorants, can give rise to an ordered arrangement of representations at the olfactory bulb level where the distances between representations are perceptually relevant.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2012. xiv, 216 p.
Trita-CSC-A, ISSN 1653-5723 ; 2012:01
feature selection, image features, pattern classification, relevance, redundancy, distributional similarity, divergence measure, genetic algorithms, clustering algorithms, annealing, olfactory coding, olfactory bulb, odorants, glomeruli, property-activity relationship, olfaction, plasticity, axonal guidance, odor category, perception, spatial coding, population coding, memory organization, odor quality
National Category
Biological Sciences
Research subject
urn:nbn:se:kth:diva-90474 (URN)978-91-7501-273-5 (ISBN)
Public defence
2012-03-16, D3, Lindstedtsvägen 5, KTH, Stockholm, 10:00 (English)
Swedish e‐Science Research Center

QC 20120224

Available from: 2012-02-24 Created: 2012-02-24 Last updated: 2013-04-09Bibliographically approved

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