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Understanding odor information segregation in the olfactory bulb by means of mitral and tufted cells.

Polese D, Martinelli E, Marco S, Di Natale C, Gutierrez-Galvez A - PLoS ONE (2014)

Bottom Line: This capacity to ignore concentration information does not preclude the olfactory system from estimating concentration itself.The results of the experiments were visualized using principal components analysis and analyzed with hierarchical clustering to unveil the structure of the high-dimensional output space.An important conclusion is also that the morphological difference between the principal neurons is not key to achieve odor information segregation.

View Article: PubMed Central - PubMed

Affiliation: Istituto per la Microelettronica e Microsistemi, Consiglio Nazionale delle Ricerche, Roma, Italy.

ABSTRACT
Odor identification is one of the main tasks of the olfactory system. It is performed almost independently from the concentration of the odor providing a robust recognition. This capacity to ignore concentration information does not preclude the olfactory system from estimating concentration itself. Significant experimental evidence has indicated that the olfactory system is able to infer simultaneously odor identity and intensity. However, it is still unclear at what level or levels of the olfactory pathway this segregation of information occurs. In this work, we study whether this odor information segregation is performed at the input stage of the olfactory bulb: the glomerular layer. To this end, we built a detailed neural model of the glomerular layer based on its known anatomical connections and conducted two simulated odor experiments. In the first experiment, the model was exposed to an odor stimulus dataset composed of six different odorants, each one dosed at six different concentrations. In the second experiment, we conducted an odor morphing experiment where a sequence of binary mixtures going from one odor to another through intermediate mixtures was presented to the model. The results of the experiments were visualized using principal components analysis and analyzed with hierarchical clustering to unveil the structure of the high-dimensional output space. Additionally, Fisher's discriminant ratio and Pearson's correlation coefficient were used to quantify odor identity and odor concentration information respectively. Our results showed that the architecture of the glomerular layer was able to mediate the segregation of odor information obtaining output spiking sequences of the principal neurons, namely the mitral and external tufted cells, strongly correlated with odor identity and concentration, respectively. An important conclusion is also that the morphological difference between the principal neurons is not key to achieve odor information segregation.

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Input odor patters and MC output activity.(A) Scores plot of the first two principal components of the input odors used to analyze the glomerular layer model. The directions of the arrows indicate increasing concentration. (B) Relative composition of odor C and E on the series of binary mixtures that simulate the morphing between the two odors. The y-axis shows the relative composition of odor C and odor E in each one of the 21 mixtures. (C) Example of mitral cell responses of a 16-glomeruli model. Different colors identify different mitral cells.
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pone-0109716-g002: Input odor patters and MC output activity.(A) Scores plot of the first two principal components of the input odors used to analyze the glomerular layer model. The directions of the arrows indicate increasing concentration. (B) Relative composition of odor C and E on the series of binary mixtures that simulate the morphing between the two odors. The y-axis shows the relative composition of odor C and odor E in each one of the 21 mixtures. (C) Example of mitral cell responses of a 16-glomeruli model. Different colors identify different mitral cells.

Mentions: In the first experiment, we exposed the glomerular model to 6 odorants at 6 different concentrations each. We assumed that each of the 16 classes of ORNs is sensitive to all the odorants but with different magnitudes, furthermore a linear relationship between concentration and response exists. Then the response of each class of ORN, input to its relevant glomerulus, is RORNodor  =  SORNCodor. The quantities SORN were randomly generated from a uniform distribution ranging from 0 pA to 40 pA. Concentrations (Codor) are dimensionless quantities in the range {0.4, 0.6, 0.8, 1, 1.2, 1.4}. Eventually, 36 vectors of responses were generated. The magnitude of the response pattern is limited to 40 pA, taking into account the saturation effect of the ORN response. The 36 vectors are shown in figure 2a.


Understanding odor information segregation in the olfactory bulb by means of mitral and tufted cells.

Polese D, Martinelli E, Marco S, Di Natale C, Gutierrez-Galvez A - PLoS ONE (2014)

Input odor patters and MC output activity.(A) Scores plot of the first two principal components of the input odors used to analyze the glomerular layer model. The directions of the arrows indicate increasing concentration. (B) Relative composition of odor C and E on the series of binary mixtures that simulate the morphing between the two odors. The y-axis shows the relative composition of odor C and odor E in each one of the 21 mixtures. (C) Example of mitral cell responses of a 16-glomeruli model. Different colors identify different mitral cells.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4214673&req=5

pone-0109716-g002: Input odor patters and MC output activity.(A) Scores plot of the first two principal components of the input odors used to analyze the glomerular layer model. The directions of the arrows indicate increasing concentration. (B) Relative composition of odor C and E on the series of binary mixtures that simulate the morphing between the two odors. The y-axis shows the relative composition of odor C and odor E in each one of the 21 mixtures. (C) Example of mitral cell responses of a 16-glomeruli model. Different colors identify different mitral cells.
Mentions: In the first experiment, we exposed the glomerular model to 6 odorants at 6 different concentrations each. We assumed that each of the 16 classes of ORNs is sensitive to all the odorants but with different magnitudes, furthermore a linear relationship between concentration and response exists. Then the response of each class of ORN, input to its relevant glomerulus, is RORNodor  =  SORNCodor. The quantities SORN were randomly generated from a uniform distribution ranging from 0 pA to 40 pA. Concentrations (Codor) are dimensionless quantities in the range {0.4, 0.6, 0.8, 1, 1.2, 1.4}. Eventually, 36 vectors of responses were generated. The magnitude of the response pattern is limited to 40 pA, taking into account the saturation effect of the ORN response. The 36 vectors are shown in figure 2a.

Bottom Line: This capacity to ignore concentration information does not preclude the olfactory system from estimating concentration itself.The results of the experiments were visualized using principal components analysis and analyzed with hierarchical clustering to unveil the structure of the high-dimensional output space.An important conclusion is also that the morphological difference between the principal neurons is not key to achieve odor information segregation.

View Article: PubMed Central - PubMed

Affiliation: Istituto per la Microelettronica e Microsistemi, Consiglio Nazionale delle Ricerche, Roma, Italy.

ABSTRACT
Odor identification is one of the main tasks of the olfactory system. It is performed almost independently from the concentration of the odor providing a robust recognition. This capacity to ignore concentration information does not preclude the olfactory system from estimating concentration itself. Significant experimental evidence has indicated that the olfactory system is able to infer simultaneously odor identity and intensity. However, it is still unclear at what level or levels of the olfactory pathway this segregation of information occurs. In this work, we study whether this odor information segregation is performed at the input stage of the olfactory bulb: the glomerular layer. To this end, we built a detailed neural model of the glomerular layer based on its known anatomical connections and conducted two simulated odor experiments. In the first experiment, the model was exposed to an odor stimulus dataset composed of six different odorants, each one dosed at six different concentrations. In the second experiment, we conducted an odor morphing experiment where a sequence of binary mixtures going from one odor to another through intermediate mixtures was presented to the model. The results of the experiments were visualized using principal components analysis and analyzed with hierarchical clustering to unveil the structure of the high-dimensional output space. Additionally, Fisher's discriminant ratio and Pearson's correlation coefficient were used to quantify odor identity and odor concentration information respectively. Our results showed that the architecture of the glomerular layer was able to mediate the segregation of odor information obtaining output spiking sequences of the principal neurons, namely the mitral and external tufted cells, strongly correlated with odor identity and concentration, respectively. An important conclusion is also that the morphological difference between the principal neurons is not key to achieve odor information segregation.

Show MeSH
Related in: MedlinePlus