Representation and recognition in vision
Representation and recognition in vision
Unsupervised Segmentation With Dynamical Units
IEEE Transactions on Neural Networks
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The relationship between structure and function is of central importance in neuroscience. Computational modeling techniques can playa crucial role in exploring this relationship. Neuroscientists have revealed an interesting patterning in the connectivity of visual cortical areas, where the receptive field sizes for feed-forward, lateral and feedback connections are monotonically increasing and the se roughly double. In this paper, we use a computational modeling approach to understand the behavior of the visual system, and show that this observed connectivity pattern can be explained via a maximization of functional metrics based on separation and segmentation accuracies. We use an optimization function based on sparse spatio-temporal encoding and faithfulness of representation to derive the dynamical behavior of a multi-layer network of oscillatory units. The network behavior can be quantified in terms of its ability to separate and segment mixtures of inputs. We vary the topological connectivity between different layers of the simulated visual network and study its effect on performance in terms of separation and segmentation accuracy. We demonstrate that the best performance occurs when the topology of the simulated system is similar to that in the primate visual cortex where the receptive field sizes of feedforward, lateral and feedback connections are monotonically increasing. This explanation of the functional significance of topological connectivity provides a new per spective for the understanding of cortical function.