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Simon M. Stringer
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Journal Articles
Publisher: Journals Gateway
Neural Computation (2007) 19 (1): 139–169.
Published: 01 January 2007
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The motion of an object (such as a wheel rotating) is seen as consistent independent of its position and size on the retina. Neurons in higher cortical visual areas respond to these global motion stimuli invariantly, but neurons in early cortical areas with small receptive fields cannot represent this motion, not only because of the aperture problem but also because they do not have invariant representations. In a unifying hypothesis with the design of the ventral cortical visual system, we propose that the dorsal visual system uses a hierarchical feedforward network architecture (V1, V2, MT, MSTd, parietal cortex) with training of the connections with a short-term memory trace associative synaptic modification rule to capture what is invariant at each stage. Simulations show that the proposal is computationally feasible, in that invariant representations of the motion flow fields produced by objects self-organize in the later layers of the architecture. The model produces invariant representations of the motion flow fields produced by global in-plane motion of an object, in-plane rotational motion, looming versus receding of the object, and object-based rotation about a principal axis. Thus, the dorsal and ventral visual systems may share some similar computational principles.
Journal Articles
Publisher: Journals Gateway
Neural Computation (2002) 14 (11): 2585–2596.
Published: 01 November 2002
Abstract
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To form view-invariant representations of objects, neurons in the inferior temporal cortex may associate together different views of an object, which tend to occur close together in time under natural viewing conditions. This can be achieved in neuronal network models of this process by using an associative learning rule with a short-term temporal memory trace. It is postulated that within a view, neurons learn representations that enable them to generalize within variations of that view. When three-dimensional (3D) objects are rotated within small angles (up to, e.g., 30 degrees), their surface features undergo geometric distortion due to the change of perspective. In this article, we show how trace learning could solve the problem of in-depth rotation-invariant object recognition by developing representations of the transforms that features undergo when they are on the surfaces of 3D objects. Moreover, we show that having learned how features on 3D objects transform geometrically as the object is rotated in depth, the network can correctly recognize novel 3D variations within a generic view of an object composed of a new combination of previously learned features. These results are demonstrated in simulations of a hierarchical network model (VisNet) of the visual system that show that it can develop representations useful for the recognition of 3D objects by forming perspective-invariant representations to allow generalization within a generic view.