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Electrosensory neural responses to natural electro-communication stimuli are distributed along a continuum

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ABSTRACT

Neural heterogeneities are seen ubiquitously within the brain and greatly complicate classification efforts. Here we tested whether the responses of an anatomically well-characterized sensory neuron population to natural stimuli could be used for functional classification. To do so, we recorded from pyramidal cells within the electrosensory lateral line lobe (ELL) of the weakly electric fish Apteronotus leptorhynchus in response to natural electro-communication stimuli as these cells can be anatomically classified into six different types. We then used two independent methodologies to functionally classify responses: one relies of reducing the dimensionality of a feature space while the other directly compares the responses themselves. Both methodologies gave rise to qualitatively similar results: while ON and OFF-type cells could easily be distinguished from one another, ELL pyramidal neuron responses are actually distributed along a continuum rather than forming distinct clusters due to heterogeneities. We discuss the implications of our results for neural coding and highlight some potential advantages.

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Summary of steps taken in order to classify neuronal responses to naturalistic communication stimuli.The Common factor analysis technique (orange) aims to reduce dimensionality by developing a linear statistical model summarising in a low dimensional space the high dimensional response space of the data. Proximities within this space can then be used determine how responses are represented in the brain (green) (i.e. discrete clustered representations or a continuous representation). In contrast, the Dynamic time warping technique (blue) permits one to directly quantify the proximity between observations via a non-linear relation among responses abstracted as time series. Raster plots are transformed into time series (PSTHs). After defining a window of comparison to be permitted between PSTHs all pairwise comparisons between observations belonging to a population are made, yielding a pairwise distance matrix, which can then be used in the same way as for the Common Factor Analysis technique (green).
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pone.0175322.g003: Summary of steps taken in order to classify neuronal responses to naturalistic communication stimuli.The Common factor analysis technique (orange) aims to reduce dimensionality by developing a linear statistical model summarising in a low dimensional space the high dimensional response space of the data. Proximities within this space can then be used determine how responses are represented in the brain (green) (i.e. discrete clustered representations or a continuous representation). In contrast, the Dynamic time warping technique (blue) permits one to directly quantify the proximity between observations via a non-linear relation among responses abstracted as time series. Raster plots are transformed into time series (PSTHs). After defining a window of comparison to be permitted between PSTHs all pairwise comparisons between observations belonging to a population are made, yielding a pairwise distance matrix, which can then be used in the same way as for the Common Factor Analysis technique (green).

Mentions: The first algorithm quantified responses of each cell in our dataset by computing a large number of features (44, see Table 1) representing various aspects of the observed responses. The dimensionality of this set was then reduced by using a Common factor analysis (CFA) model (see Methods). It is important to note that CFA, like all dimensionality reduction algorithms, can only account for a portion of the variance displayed by the original dataset. In this case, we found that an 8-factor solution accounted for 78% of the variance. We then applied a single linkage agglomerative hierarchical clustering algorithm to the pairwise distance matrix constructed using the Euclidian distances between observations in the factor space (see Methods and Fig 3, left column).


Electrosensory neural responses to natural electro-communication stimuli are distributed along a continuum
Summary of steps taken in order to classify neuronal responses to naturalistic communication stimuli.The Common factor analysis technique (orange) aims to reduce dimensionality by developing a linear statistical model summarising in a low dimensional space the high dimensional response space of the data. Proximities within this space can then be used determine how responses are represented in the brain (green) (i.e. discrete clustered representations or a continuous representation). In contrast, the Dynamic time warping technique (blue) permits one to directly quantify the proximity between observations via a non-linear relation among responses abstracted as time series. Raster plots are transformed into time series (PSTHs). After defining a window of comparison to be permitted between PSTHs all pairwise comparisons between observations belonging to a population are made, yielding a pairwise distance matrix, which can then be used in the same way as for the Common Factor Analysis technique (green).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5383285&req=5

pone.0175322.g003: Summary of steps taken in order to classify neuronal responses to naturalistic communication stimuli.The Common factor analysis technique (orange) aims to reduce dimensionality by developing a linear statistical model summarising in a low dimensional space the high dimensional response space of the data. Proximities within this space can then be used determine how responses are represented in the brain (green) (i.e. discrete clustered representations or a continuous representation). In contrast, the Dynamic time warping technique (blue) permits one to directly quantify the proximity between observations via a non-linear relation among responses abstracted as time series. Raster plots are transformed into time series (PSTHs). After defining a window of comparison to be permitted between PSTHs all pairwise comparisons between observations belonging to a population are made, yielding a pairwise distance matrix, which can then be used in the same way as for the Common Factor Analysis technique (green).
Mentions: The first algorithm quantified responses of each cell in our dataset by computing a large number of features (44, see Table 1) representing various aspects of the observed responses. The dimensionality of this set was then reduced by using a Common factor analysis (CFA) model (see Methods). It is important to note that CFA, like all dimensionality reduction algorithms, can only account for a portion of the variance displayed by the original dataset. In this case, we found that an 8-factor solution accounted for 78% of the variance. We then applied a single linkage agglomerative hierarchical clustering algorithm to the pairwise distance matrix constructed using the Euclidian distances between observations in the factor space (see Methods and Fig 3, left column).

View Article: PubMed Central - PubMed

ABSTRACT

Neural heterogeneities are seen ubiquitously within the brain and greatly complicate classification efforts. Here we tested whether the responses of an anatomically well-characterized sensory neuron population to natural stimuli could be used for functional classification. To do so, we recorded from pyramidal cells within the electrosensory lateral line lobe (ELL) of the weakly electric fish Apteronotus leptorhynchus in response to natural electro-communication stimuli as these cells can be anatomically classified into six different types. We then used two independent methodologies to functionally classify responses: one relies of reducing the dimensionality of a feature space while the other directly compares the responses themselves. Both methodologies gave rise to qualitatively similar results: while ON and OFF-type cells could easily be distinguished from one another, ELL pyramidal neuron responses are actually distributed along a continuum rather than forming distinct clusters due to heterogeneities. We discuss the implications of our results for neural coding and highlight some potential advantages.

No MeSH data available.


Related in: MedlinePlus