Abstract
In humans, the problem of coordinate transformations is far from being completely understood. The problem is often addressed using a mix of supervised and unsupervised learning techniques. In this paper, we propose a novel learning framework which requires only unsupervised learning. We design a neural architecture that models the visual dorsal pathway and learns coordinate transformations in a computer simulation comprising an eye, a head and an arm (each entailing one degree of freedom). The learning is carried out in two stages. First, we train a posterior parietal cortex (PPC) model to learn different frames of reference transformations. And second, we train a head-centered neural layer to compute the position of an arm with respect to the head. Our results show the self-organization of the receptive fields (gain fields) in the PPC model and the self-tuning of the response of the head-centered population of neurons.
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Mutti, F., Marques, H.G., Gini, G. (2013). A Model of the Visual Dorsal Pathway for Computing Coordinate Transformations: An Unsupervised Approach. In: Chella, A., Pirrone, R., Sorbello, R., Jóhannsdóttir, K. (eds) Biologically Inspired Cognitive Architectures 2012. Advances in Intelligent Systems and Computing, vol 196. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34274-5_42
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DOI: https://doi.org/10.1007/978-3-642-34274-5_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34273-8
Online ISBN: 978-3-642-34274-5
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