A Structured Model of Video Reproduces Primary Visual Cortical Organisation

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

An Active Efficient Coding Model of Binocular Vision Development Under Normal and Abnormal Rearing Conditions

The development of binocular vision encompasses the formation of binocular receptive fields tuned to different disparities and the calibration of accurate vergence eye movements. Experiments have shown that this development is impaired when the animal is exposed to certain abnormal rearing conditions such as growing up in an environment that is deprived of horizontal or vertical edges. Here we test the effect of abnormal rearing conditions on a recently proposed computational model of binocular development. The model is formulated in the Active Efficient Coding framework, a generalization of classic efficient coding ideas to active perception. We show that abnormal rearing conditions lead to differences in the model’s development that qualitatively match those seen in animal experiments. Furthermore, the model predicts systematic changes in vergence accuracy due to abnormal rearing. We discuss implications of the model for the treatment of developmental disorders of binocular vision such as amblyopia and strabismus. © 2018, Springer Nature Switzerland AG