Coding of objects in low-level visual cortical areas

Authors:
N. R. Taylor;M. Hartley;J. G. Taylor
Affiliations:
Department of Mathematics, King’s College, London, UK;Department of Mathematics, King’s College, London, UK;Department of Mathematics, King’s College, London, UK
Venue:
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Year:
2005

Citing 2
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Abstract

We develop a neural network architecture to help model the creation of visual temporal object representations. We take visual input to be hard-wired up to and including V1 (as an orientation-filtering system). We then develop architectures for afferents to V2 and thence to V4, both of which are trained by a causal Hebbian law. We use an incremental approach, using sequences of increasingly complex stimuli at an increasing level of the hierarchy. The V2 representations are shown to encode angles, and V4 is found sensitive to shapes embedded in figures. These results are compared to recent experimental data, supporting the incremental training scheme and associated architecture.