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Profound context-dependent plasticity of mitral cell responses in olfactory bulb.

Doucette W, Restrepo D - PLoS Biol. (2008)

Bottom Line: The response changes occur in a manner that increases the ability of the circuit to convey information necessary to discriminate among closely related odors.Remarkably, a switch between which of the two odors is rewarded causes mitral cells to switch the polarity of their divergent responses.Taken together these results redefine the function of the OB as a transiently modifiable (active) filter, shaping early odor representations in behaviorally meaningful ways.

View Article: PubMed Central - PubMed

Affiliation: Department of Cell and Developmental Biology, Neuroscience Program, Rocky Mountain Taste and Smell Center, University of Colorado Denver Anschutz Medical Campus, Aurora, Colorado, United States of America.

ABSTRACT
On the basis of its primary circuit it has been postulated that the olfactory bulb (OB) is analogous to the retina in mammals. In retina, repeated exposure to the same visual stimulus results in a neural representation that remains relatively stable over time, even as the meaning of that stimulus to the animal changes. Stability of stimulus representation at early stages of processing allows for unbiased interpretation of incoming stimuli by higher order cortical centers. The alternative is that early stimulus representation is shaped by previously derived meaning, which could allow more efficient sampling of odor space providing a simplified yet biased interpretation of incoming stimuli. This study helps place the olfactory system on this continuum of subjective versus objective early sensory representation. Here we show that odor responses of the output cells of the OB, mitral cells, change transiently during a go-no-go odor discrimination task. The response changes occur in a manner that increases the ability of the circuit to convey information necessary to discriminate among closely related odors. Remarkably, a switch between which of the two odors is rewarded causes mitral cells to switch the polarity of their divergent responses. Taken together these results redefine the function of the OB as a transiently modifiable (active) filter, shaping early odor representations in behaviorally meaningful ways.

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Related in: MedlinePlus

PCA of the Responses of the 95 Divergent SMCs Calculated for the First, Best, and Last BlocksThe input to the PCA was the number of spikes per 0.15-s bin for all the trials in each block for the 95 divergent SMCs. The PCA algorithm computes principal components (linear combinations of the input variables) ranked in order of how much of the variance they account for in the dataset; the first few principal components (PC1 and PC2 are shown in [A] and [B]) explain most of the variance and they can be thought of as the firing rate of newly formed units containing a large amount of the information on odor divergence. Note that the principal components are dimensionless and therefore, no units are shown in the graphs.(A) Scatterplots displaying points denoting the location in two-dimensional principal component space of each trial in the best block. The left panel displays the distribution of points at a time before exposure to odor (−0.3 s) and the right panel shows the distribution at a time point during odor exposure (1.5 s). The red points are trials where the animals were exposed to the reinforced odor and the blue points are trials with the unreinforced odor.(B) The entire mean trajectory through time in two -dimensional principal component space is shown for the first (left), best (center), and last (right) blocks. In order to generate the mean trajectory, the mean location of all red and blue points in graphs such as those shown in (A), but spanning the entire time course from −2.5 to 4.5 s was calculated and plotted as a continuous line. Zero seconds is the time when the diverting valve is turned off and the odor is directed towards the animal. The red line is the trajectory for trials with the reinforced odor and the blue line is the trajectory for trials with the unreinforced odor. The trajectories hover around the origin (0.0) during the period before the odor valve opens (It < 0 s) and then move outward in opposite directions after the diverting valve is opened (0 to 2.5 s) during the period when the animal is exposed to the odor. The numbers adjacent to specific points in the trajectory for the best block denote different times in the trial and are shown to facilitate understanding of how the points move through time. The numbers stand for the following times: (1) 0.75 s; (2) 1.05 s; (3) 1.65 s; (4) 2.1 s.(C) The blue line shows the change during the time course of a trial of the mean of all pairwise euclidean distances in PCA space between points that belonged to trials where the animals were exposed to different odors (reinforced versus unreinforced odors). Pairwise euclidean distances are calculated in 95-dimensional principal component space. To calculate the delta distances shown by the blue line the mean of the pairwise distances between points for trials where the animals were stimulated with the same odor were subtracted from the mean of pairwise distances for points for trials where animals were stimulated with different odors. The red traces represent the SEM.(D) Logarithm of the p-value calculated with a rank sum test performed at every 0.15-s time bin for the difference in the pairwise PCA distances between points for trials where the animals were stimulated with the same odor compared to pairwise distances for points for trials where animals were stimulated with different odors. If the logarithm of the p-value fell below −5, the point was assigned a value of −5. The horizontal red line is the logarithm of 0.05. The vertical black lines are the times when the p-value drops below 0.05, and the red vertical lines are the median of the behavioral discrimination times shown in Figure 8B.
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pbio-0060258-g009: PCA of the Responses of the 95 Divergent SMCs Calculated for the First, Best, and Last BlocksThe input to the PCA was the number of spikes per 0.15-s bin for all the trials in each block for the 95 divergent SMCs. The PCA algorithm computes principal components (linear combinations of the input variables) ranked in order of how much of the variance they account for in the dataset; the first few principal components (PC1 and PC2 are shown in [A] and [B]) explain most of the variance and they can be thought of as the firing rate of newly formed units containing a large amount of the information on odor divergence. Note that the principal components are dimensionless and therefore, no units are shown in the graphs.(A) Scatterplots displaying points denoting the location in two-dimensional principal component space of each trial in the best block. The left panel displays the distribution of points at a time before exposure to odor (−0.3 s) and the right panel shows the distribution at a time point during odor exposure (1.5 s). The red points are trials where the animals were exposed to the reinforced odor and the blue points are trials with the unreinforced odor.(B) The entire mean trajectory through time in two -dimensional principal component space is shown for the first (left), best (center), and last (right) blocks. In order to generate the mean trajectory, the mean location of all red and blue points in graphs such as those shown in (A), but spanning the entire time course from −2.5 to 4.5 s was calculated and plotted as a continuous line. Zero seconds is the time when the diverting valve is turned off and the odor is directed towards the animal. The red line is the trajectory for trials with the reinforced odor and the blue line is the trajectory for trials with the unreinforced odor. The trajectories hover around the origin (0.0) during the period before the odor valve opens (It < 0 s) and then move outward in opposite directions after the diverting valve is opened (0 to 2.5 s) during the period when the animal is exposed to the odor. The numbers adjacent to specific points in the trajectory for the best block denote different times in the trial and are shown to facilitate understanding of how the points move through time. The numbers stand for the following times: (1) 0.75 s; (2) 1.05 s; (3) 1.65 s; (4) 2.1 s.(C) The blue line shows the change during the time course of a trial of the mean of all pairwise euclidean distances in PCA space between points that belonged to trials where the animals were exposed to different odors (reinforced versus unreinforced odors). Pairwise euclidean distances are calculated in 95-dimensional principal component space. To calculate the delta distances shown by the blue line the mean of the pairwise distances between points for trials where the animals were stimulated with the same odor were subtracted from the mean of pairwise distances for points for trials where animals were stimulated with different odors. The red traces represent the SEM.(D) Logarithm of the p-value calculated with a rank sum test performed at every 0.15-s time bin for the difference in the pairwise PCA distances between points for trials where the animals were stimulated with the same odor compared to pairwise distances for points for trials where animals were stimulated with different odors. If the logarithm of the p-value fell below −5, the point was assigned a value of −5. The horizontal red line is the logarithm of 0.05. The vertical black lines are the times when the p-value drops below 0.05, and the red vertical lines are the median of the behavioral discrimination times shown in Figure 8B.

Mentions: As shown in Figure 8D, in about 20% of the SMCs the firing rate diverges during the best block before the animal makes the behavioral decision. If all the divergent SMCs are used, would the information content be enough to allow an unbiased observer to make the decision before the animal has to make the behavioral decision? We used principal component analysis (PCA) to answer this question and to provide lower dimensional representation of how the information content provided by the ensemble of SMCs varied as the animals learned to discriminate rewarded and unrewarded odors. In previous studies PCA has proved to be an effective tool to obtain such information [17,40]. The input to the PCA was the number of spikes fired at 0.15-s intervals for all units that diverged in firing between rewarded and unrewarded odors (95 units from eight animals and 19 sessions, see Materials and Methods). The PCA algorithm computes an orthogonal linear transformation of a dataset into linear combinations of the input variables (principal components) ranked in order of how much of the variance they account for in the dataset; the first few principal components explain most of the variance and they can be thought of as new units containing the majority of the information on odor divergence. Figure 9A displays a scatterplot where the location in a two-dimensional space made up of principal components 1 and 2 is shown for each trial where the animal was exposed to the reinforced odor (red) or unreinforced odor (blue). The plot on the left of Figure 9A is a scatterplot computed at a time before the animal was exposed to the odor. As expected, at this time in the trial the blue and red points overlap and therefore the two principal components contain no information on how to differentiate between odors. In contrast, the panel on the right side in Figure 9A shows the scatterplot at a time point during odor exposure. In this case, there is a segregation of the red and blue points suggesting that there is enough information contained in the two principal components to be able to differentiate between the reinforced and unreinforced odors. Figure 9B shows the trajectory over time for the mean location (calculated over all the trials in each block) for the reinforced (red) and unreinforced (blue) responses plotted in the two-dimensional space defined by principal components 1 and 2. The data are plotted for three blocks: the first and last blocks, and the “best block” defined as the block where units showed the most divergence between odors in each experiment (lowest p-values in the odor divergence t-test). As shown the blue and red trajectories overlap in the first block, become distinct in the best block, and begin overlapping again in the last block.


Profound context-dependent plasticity of mitral cell responses in olfactory bulb.

Doucette W, Restrepo D - PLoS Biol. (2008)

PCA of the Responses of the 95 Divergent SMCs Calculated for the First, Best, and Last BlocksThe input to the PCA was the number of spikes per 0.15-s bin for all the trials in each block for the 95 divergent SMCs. The PCA algorithm computes principal components (linear combinations of the input variables) ranked in order of how much of the variance they account for in the dataset; the first few principal components (PC1 and PC2 are shown in [A] and [B]) explain most of the variance and they can be thought of as the firing rate of newly formed units containing a large amount of the information on odor divergence. Note that the principal components are dimensionless and therefore, no units are shown in the graphs.(A) Scatterplots displaying points denoting the location in two-dimensional principal component space of each trial in the best block. The left panel displays the distribution of points at a time before exposure to odor (−0.3 s) and the right panel shows the distribution at a time point during odor exposure (1.5 s). The red points are trials where the animals were exposed to the reinforced odor and the blue points are trials with the unreinforced odor.(B) The entire mean trajectory through time in two -dimensional principal component space is shown for the first (left), best (center), and last (right) blocks. In order to generate the mean trajectory, the mean location of all red and blue points in graphs such as those shown in (A), but spanning the entire time course from −2.5 to 4.5 s was calculated and plotted as a continuous line. Zero seconds is the time when the diverting valve is turned off and the odor is directed towards the animal. The red line is the trajectory for trials with the reinforced odor and the blue line is the trajectory for trials with the unreinforced odor. The trajectories hover around the origin (0.0) during the period before the odor valve opens (It < 0 s) and then move outward in opposite directions after the diverting valve is opened (0 to 2.5 s) during the period when the animal is exposed to the odor. The numbers adjacent to specific points in the trajectory for the best block denote different times in the trial and are shown to facilitate understanding of how the points move through time. The numbers stand for the following times: (1) 0.75 s; (2) 1.05 s; (3) 1.65 s; (4) 2.1 s.(C) The blue line shows the change during the time course of a trial of the mean of all pairwise euclidean distances in PCA space between points that belonged to trials where the animals were exposed to different odors (reinforced versus unreinforced odors). Pairwise euclidean distances are calculated in 95-dimensional principal component space. To calculate the delta distances shown by the blue line the mean of the pairwise distances between points for trials where the animals were stimulated with the same odor were subtracted from the mean of pairwise distances for points for trials where animals were stimulated with different odors. The red traces represent the SEM.(D) Logarithm of the p-value calculated with a rank sum test performed at every 0.15-s time bin for the difference in the pairwise PCA distances between points for trials where the animals were stimulated with the same odor compared to pairwise distances for points for trials where animals were stimulated with different odors. If the logarithm of the p-value fell below −5, the point was assigned a value of −5. The horizontal red line is the logarithm of 0.05. The vertical black lines are the times when the p-value drops below 0.05, and the red vertical lines are the median of the behavioral discrimination times shown in Figure 8B.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2573932&req=5

pbio-0060258-g009: PCA of the Responses of the 95 Divergent SMCs Calculated for the First, Best, and Last BlocksThe input to the PCA was the number of spikes per 0.15-s bin for all the trials in each block for the 95 divergent SMCs. The PCA algorithm computes principal components (linear combinations of the input variables) ranked in order of how much of the variance they account for in the dataset; the first few principal components (PC1 and PC2 are shown in [A] and [B]) explain most of the variance and they can be thought of as the firing rate of newly formed units containing a large amount of the information on odor divergence. Note that the principal components are dimensionless and therefore, no units are shown in the graphs.(A) Scatterplots displaying points denoting the location in two-dimensional principal component space of each trial in the best block. The left panel displays the distribution of points at a time before exposure to odor (−0.3 s) and the right panel shows the distribution at a time point during odor exposure (1.5 s). The red points are trials where the animals were exposed to the reinforced odor and the blue points are trials with the unreinforced odor.(B) The entire mean trajectory through time in two -dimensional principal component space is shown for the first (left), best (center), and last (right) blocks. In order to generate the mean trajectory, the mean location of all red and blue points in graphs such as those shown in (A), but spanning the entire time course from −2.5 to 4.5 s was calculated and plotted as a continuous line. Zero seconds is the time when the diverting valve is turned off and the odor is directed towards the animal. The red line is the trajectory for trials with the reinforced odor and the blue line is the trajectory for trials with the unreinforced odor. The trajectories hover around the origin (0.0) during the period before the odor valve opens (It < 0 s) and then move outward in opposite directions after the diverting valve is opened (0 to 2.5 s) during the period when the animal is exposed to the odor. The numbers adjacent to specific points in the trajectory for the best block denote different times in the trial and are shown to facilitate understanding of how the points move through time. The numbers stand for the following times: (1) 0.75 s; (2) 1.05 s; (3) 1.65 s; (4) 2.1 s.(C) The blue line shows the change during the time course of a trial of the mean of all pairwise euclidean distances in PCA space between points that belonged to trials where the animals were exposed to different odors (reinforced versus unreinforced odors). Pairwise euclidean distances are calculated in 95-dimensional principal component space. To calculate the delta distances shown by the blue line the mean of the pairwise distances between points for trials where the animals were stimulated with the same odor were subtracted from the mean of pairwise distances for points for trials where animals were stimulated with different odors. The red traces represent the SEM.(D) Logarithm of the p-value calculated with a rank sum test performed at every 0.15-s time bin for the difference in the pairwise PCA distances between points for trials where the animals were stimulated with the same odor compared to pairwise distances for points for trials where animals were stimulated with different odors. If the logarithm of the p-value fell below −5, the point was assigned a value of −5. The horizontal red line is the logarithm of 0.05. The vertical black lines are the times when the p-value drops below 0.05, and the red vertical lines are the median of the behavioral discrimination times shown in Figure 8B.
Mentions: As shown in Figure 8D, in about 20% of the SMCs the firing rate diverges during the best block before the animal makes the behavioral decision. If all the divergent SMCs are used, would the information content be enough to allow an unbiased observer to make the decision before the animal has to make the behavioral decision? We used principal component analysis (PCA) to answer this question and to provide lower dimensional representation of how the information content provided by the ensemble of SMCs varied as the animals learned to discriminate rewarded and unrewarded odors. In previous studies PCA has proved to be an effective tool to obtain such information [17,40]. The input to the PCA was the number of spikes fired at 0.15-s intervals for all units that diverged in firing between rewarded and unrewarded odors (95 units from eight animals and 19 sessions, see Materials and Methods). The PCA algorithm computes an orthogonal linear transformation of a dataset into linear combinations of the input variables (principal components) ranked in order of how much of the variance they account for in the dataset; the first few principal components explain most of the variance and they can be thought of as new units containing the majority of the information on odor divergence. Figure 9A displays a scatterplot where the location in a two-dimensional space made up of principal components 1 and 2 is shown for each trial where the animal was exposed to the reinforced odor (red) or unreinforced odor (blue). The plot on the left of Figure 9A is a scatterplot computed at a time before the animal was exposed to the odor. As expected, at this time in the trial the blue and red points overlap and therefore the two principal components contain no information on how to differentiate between odors. In contrast, the panel on the right side in Figure 9A shows the scatterplot at a time point during odor exposure. In this case, there is a segregation of the red and blue points suggesting that there is enough information contained in the two principal components to be able to differentiate between the reinforced and unreinforced odors. Figure 9B shows the trajectory over time for the mean location (calculated over all the trials in each block) for the reinforced (red) and unreinforced (blue) responses plotted in the two-dimensional space defined by principal components 1 and 2. The data are plotted for three blocks: the first and last blocks, and the “best block” defined as the block where units showed the most divergence between odors in each experiment (lowest p-values in the odor divergence t-test). As shown the blue and red trajectories overlap in the first block, become distinct in the best block, and begin overlapping again in the last block.

Bottom Line: The response changes occur in a manner that increases the ability of the circuit to convey information necessary to discriminate among closely related odors.Remarkably, a switch between which of the two odors is rewarded causes mitral cells to switch the polarity of their divergent responses.Taken together these results redefine the function of the OB as a transiently modifiable (active) filter, shaping early odor representations in behaviorally meaningful ways.

View Article: PubMed Central - PubMed

Affiliation: Department of Cell and Developmental Biology, Neuroscience Program, Rocky Mountain Taste and Smell Center, University of Colorado Denver Anschutz Medical Campus, Aurora, Colorado, United States of America.

ABSTRACT
On the basis of its primary circuit it has been postulated that the olfactory bulb (OB) is analogous to the retina in mammals. In retina, repeated exposure to the same visual stimulus results in a neural representation that remains relatively stable over time, even as the meaning of that stimulus to the animal changes. Stability of stimulus representation at early stages of processing allows for unbiased interpretation of incoming stimuli by higher order cortical centers. The alternative is that early stimulus representation is shaped by previously derived meaning, which could allow more efficient sampling of odor space providing a simplified yet biased interpretation of incoming stimuli. This study helps place the olfactory system on this continuum of subjective versus objective early sensory representation. Here we show that odor responses of the output cells of the OB, mitral cells, change transiently during a go-no-go odor discrimination task. The response changes occur in a manner that increases the ability of the circuit to convey information necessary to discriminate among closely related odors. Remarkably, a switch between which of the two odors is rewarded causes mitral cells to switch the polarity of their divergent responses. Taken together these results redefine the function of the OB as a transiently modifiable (active) filter, shaping early odor representations in behaviorally meaningful ways.

Show MeSH
Related in: MedlinePlus