Year: 2016
Type: article
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.
Source: PLoS Computational Biology
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,043
Funders: National Science Foundation, Eye and Ear Foundation of Pittsburgh, Research to Prevent Blindness, Carnegie Mellon University, National Institutes of Health, National Defense Science and Engineering Graduate, National Eye Institute, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Awards: NCS-FO: The Structure of Neural Variability During Motor Learning, G205123881, G2492881341, G3158353629, Dynamic Mechanisms of active vision in prefrontal cortex, Shaping Neural Population Dynamics to Facilitate Learning, G6983491799, G7791925507, G9000095079, G9000115005, G9000164991, Core Grant for Vision Research
OpenAlex ID: W2560367192