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Fast heterosynaptic learning in a robot food retrieval task inspired by the limbic system

Authors
Journal
Biosystems
0303-2647
Publisher
Elsevier
Publication Date
Volume
89
Identifiers
DOI: 10.1016/j.biosystems.2006.04.026
Keywords
  • Heterosynaptic Learning
  • Hebbian Learning
  • Limbic System
  • Robotics
  • Closed Loop

Abstract

Abstract Hebbian learning is the most prominent paradigm in correlation based learning: if pre- and postsynaptic activity coincides the weight of the synapse is strengthened. Hebbian learning however, is not stable because of an autocorrelation term which causes the weights to grow exponentially. The standard solution would be to compensate the autocorrelation term. However, in this work we present a heterosynaptic learning rule which does not have an autocorrelation term and therefore does not show the instability of Hebbian learning. Consequently our heterosynaptic learning is much more stable than the classical Hebbian learning. The performance of our learning rule is demonstrated in a model which is inspired by the limbic system where an agent has to retrieve food.

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