Abstract
This paper describes a self-organizing neural network that learns a body-centered representation of 3-D target positions. This representation remains invariant under head and eye movements, and is a key component of sensory-motor systems for producing motor equivalent reaches to targets [1]. Learning requires no teacher, instead utilizing information gained from an action-perception cycle in which head movements are made while a stationary target is foveated. Because the spatial representations used relate closely to neck anatomy, the network learns very rapidly, converging after foveating only 200 targets.
This work was supported in part by grants NSF IRI-87-16960, NSF IRI-90-24877, and AFOSR F49620-92-J-0499.
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References
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© 1993 Springer-Verlag London Limited
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Bullock, D., Greve, D., Grossberg, S., Guenther, F.H. (1993). A Self-organizing Neural Network for Learning A Body-centered Invariant Representation of 3-D Target Position. In: Gielen, S., Kappen, B. (eds) ICANN ’93. ICANN 1993. Springer, London. https://doi.org/10.1007/978-1-4471-2063-6_21
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DOI: https://doi.org/10.1007/978-1-4471-2063-6_21
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