Hierarchical representation of shapes in visual cortex—from localized features to figural shape segregation

Abstract

Visual structures in the environment are segmented into image regions and those combined to a representation of surfaces and prototypical objects. Such a perceptual organization is performed by complex neural mechanisms in the visual cortex of primates. Multiple mutually connected areas in the ventral cortical pathway receive visual input and extract local form features that are subsequently grouped into increasingly complex, more meaningful image elements. Such a distributed network of processing must be capable to make accessible highly articulated changes in shape boundary as well as very subtle curvature changes that contribute to the perception of an object. We propose a recurrent computational network architecture that utilizes hierarchical distributed representations of shape features to encode surface and object boundary over different scales of resolution. Our model makes use of neural mechanisms that model the processing capabilities of early and intermediate stages in visual cortex, namely areas V1–V4 and IT. We suggest that multiple specialized component representations interact by feedforward hierarchical processing that is combined with feedback signals driven by representations generated at higher stages. Based on this, global configurational as well as local information is made available to distinguish changes in the object's contour. Once the outline of a shape has been established, contextual contour configurations are used to assign border ownership directions and thus achieve segregation of figure and ground. The model, thus, proposes how separate mechanisms contribute to distributed hierarchical cortical shape representation and combine with processes of figure-ground segregation. Our model is probed with a selection of stimuli to illustrate processing results at different processing stages. We especially highlight how modulatory feedback connections contribute to the processing of visual input at various stages in the processing hierarchy.

Space reconstruction by primary visual cortex activity: a parallel, non-computational mechanism of object representation

“…38,No. 4 characterize many objects and yet are invariant to the countless global changes in the objects' acute appearance (and this is a very big if), such a system can, at best, map inputs (images) to outputs (labels, actions) in computer programs or generate non-conscious responses such as those assumed to be generated by the visual dorsal stream.…”

“…38,No. 4 orientation information, so how can they also encode the various spatial patterns that were averaged to yield individual cells' orientation selectivity?…”

“…38,No. 4 set of hundreds of faces, each presented at various sizes, tilts, positions, and contrasts.…”

“…Thus, the fact that face cell ensembles cannot, in all probability, represent hundreds of different faces yet be indifferent to the enormous global Opinion Trends in Neurosciences April 2015, Vol. 38,No. 4 changes in individual appearance poses no difficulty; it simply means that the acute versions of any given face are perceived through V1 responses and that the extra information needed for their holistic perception and recognition is supplied by other cortical areas.…”

“…Physiology-based models, either those stressing feedforward mechanisms [33][34][35] or those adding top-down or lateral interactions related to attention, detail scrutiny, or awareness [36][37][38], have at their core a process of hierarchical convergence leading to feature elaboration. Other models that are only loosely patterned after visual cortex physiology, while emphasizing various aspects of network interactions, are also assuming that objects are ultimately represented by cell assemblies generated by hierarchical convergence [39][40][41] (Box 2).…”