Neural network model develops border ownership representation through visually guided learning

- Border-ownership representations develop through visually guided learning in a model.
- Unsupervised learning mechanisms of the hypothetical grouping circuits are presented.
- Top-down connections from V4 guide competitive learning in the lower area in V2.

As Rubin's famous vase demonstrates, our visual perception tends to assign luminance contrast borders to one or other of the adjacent image regions. Experimental evidence for the neuronal coding of such border-ownership in the primate visual system has been reported in neurophysiology. We have investigated exactly how such neural circuits may develop through visually-guided learning. More specifically, we have investigated through computer simulation how top-down connections may play a fundamental role in the development of border ownership representations in the early cortical visual layers V1/V2. Our model consists of a hierarchy of competitive neuronal layers, with both bottom-up and top-down synaptic connections between successive layers, and the synaptic connections are self-organised by a biologically plausible, temporal trace learning rule during training on differently shaped visual objects. The simulations reported in this paper have demonstrated that top-down connections may help to guide competitive learning in lower layers, thus driving the formation of lower level (border ownership) visual representations in V1/V2 that are modulated by higher level (object boundary element) representations in V4. Lastly we investigate the limitations of our model in the more general situation where multiple objects are presented to the network simultaneously.