A Structured Model of Video Reproduces Primary Visual Cortical Organisation.
PLOS COMPUT BIOL
, Article e1000495. 10.1371/journal.pcbi.1000495.
The visual system must learn to infer the presence of objects and features in the world from the images it encounters, and as such it must, either implicitly or explicitly, model the way these elements interact to create the image. Do the response properties of cells in the mammalian visual system reflect this constraint? To address this question, we constructed a probabilistic model in which the identity and attributes of simple visual elements were represented explicitly and learnt the parameters of this model from unparsed, natural video sequences. After learning, the behaviour and grouping of variables in the probabilistic model corresponded closely to functional and anatomical properties of simple and complex cells in the primary visual cortex (V1). In particular, feature identity variables were activated in a way that resembled the activity of complex cells, while feature attribute variables responded much like simple cells. Furthermore, the grouping of the attributes within the model closely parallelled the reported anatomical grouping of simple cells in cat V1. Thus, this generative model makes explicit an interpretation of complex and simple cells as elements in the segmentation of a visual scene into basic independent features, along with a parametrisation of their moment-by-moment appearances. We speculate that such a segmentation may form the initial stage of a hierarchical system that progressively separates the identity and appearance of more articulated visual elements, culminating in view-invariant object recognition.
|Title:||A Structured Model of Video Reproduces Primary Visual Cortical Organisation|
|Open access status:||An open access version is available from UCL Discovery|
|Additional information:||© 2009 Berkes et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This work has been supported by the Gatsby Charitable Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.|
|Keywords:||SLOW FEATURE ANALYSIS, CELL RECEPTIVE-FIELDS, INDEPENDENT COMPONENT ANALYSIS, INVARIANT OBJECT RECOGNITION, NATURAL IMAGES, COMPLEX CELLS, MAXIMUM-LIKELIHOOD, BAYESIAN-INFERENCE, SPATIAL STRUCTURE, ORIENTATION MAPS|
|UCL classification:||UCL > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neuroscience Unit|
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