Abstract
This chapter describes some recent results about biological models of unsupervised, realtime, error-based learning. In particular, we describe a new model called a Vector Associative Map, or VAM, and illustrate it with examples drawn from the learning of multidimensional associative maps and adaptive sensory-motor control.
Supported in part by the National Science Foundation.
Supported in part by the Air Force Office of Scientific Research (AFOSR 90-0175), DARPA (AFOSR 90-0083), and the National Science Foundation (NSF IRI-87-6960).
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Gaudiano, P., Grossberg, S. (1991). Self-Organization of Spatial Representations and Arm Trajectory Controllers by Vector Associative Maps Energized by Cyclic Random Generators. In: Babloyantz, A. (eds) Self-Organization, Emerging Properties, and Learning. NATO ASI Series, vol 260. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-3778-6_13
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DOI: https://doi.org/10.1007/978-1-4615-3778-6_13
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