Abstract
Dimensionality reduction has been applied in various brain areas to study the activity of populations of neurons. To interpret the outputs of dimensionality reduction, it is important to first understand its outputs for brain areas for which the relationship between the stimulus and neural response is well characterized. Here, we applied principal component analysis (PCA) to trial-averaged neural responses in macaque primary visual cortex (V1) to study two fundamental, population-level questions. First, we characterized how neural complexity relates to stimulus complexity, where complexity is measured using relative comparisons of dimensionality. Second, we assessed the extent to which responses to different stimuli occupy similar dimensions of the population activity space using a novel statistical method. For comparison, we performed the same dimensionality reduction analyses on the activity of a recently-proposed V1 receptive field model and a deep convolutional neural network. Our results show that the dimensionality of the population response changes systematically with alterations in the properties and complexity of the visual stimulus.
Highlights
Dimensionality reduction has been applied to neural population activity to study decision making [1, 2], motor control [3,4,5], olfaction [6], working memory [7, 8], visual attention [9], audition [10], rule learning [11], speech [12], and more
To aid in interpreting the outputs of dimensionality reduction, it is important to vary the inputs to a brain area and ask whether the outputs of dimensionality reduction change in a sensible manner, which has not yet been shown
We recorded the activity of tens of neurons in the primary visual cortex (V1) of PLOS Computational Biology | DOI:10.1371/journal.pcbi
Summary
Dimensionality reduction has been applied to neural population activity to study decision making [1, 2], motor control [3,4,5], olfaction [6], working memory [7, 8], visual attention [9], audition [10], rule learning [11], speech [12], and more (for a review, see [13]). Dimensionality reduction is applied in brain areas for which the relationship between neural activity and external variables, such as the sensory stimulus or behavior, is not well characterized. This is the setting in which dimensionality reduction may be most beneficial because it allows one to relate the activity of a neuron to the activity of other recorded neurons, without needing to assume a moment-by-moment relationship with external variables. To aid in interpreting the outputs of dimensionality reduction in such settings, it is important to vary the inputs to a brain area and ask whether the outputs of dimensionality reduction change in a sensible way. It is currently unknown how the similarity of the dimensions being occupied by the population activity changes with the similarity of the stimuli
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