Visualizing Topographic Independent Component Analysis with Movies
This work provides a visualization tool for understanding higher-order dependencies in image statistics, which is incremental for computational neuroscience and image processing.
The researchers tackled the problem of visualizing the time course of topographic independent component analysis (TICA) basis activations in response to movie stimuli, finding that these activations are often clustered and move continuously, resembling activity patterns in the visual cortex.
Independent component analysis (ICA) has often been used as a tool to model natural image statistics by separating multivariate signals in the image into components that are assumed to be independent. However, these estimated components oftentimes have higher order dependencies, such as co-activation of components, that are not accounted for in the model. Topographic independent component analysis(TICA), a modification of ICA, takes into account higher order dependencies and orders components topographically as a function of dependence. Here, we aim to visualize the time course of TICA basis activations to movie stimuli. We find that the activity of TICA bases are often clustered and move continuously, potentially resembling activity of topographically organized cells in the visual cortex.