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• W2143245633 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." @default.
• W2143245633 created "2016-06-24" @default.
• W2143245633 creator A5023811677 @default.
• W2143245633 creator A5091850362 @default.
• W2143245633 date "2007-05-01" @default.
• W2143245633 modified "2023-09-25" @default.
• W2143245633 title "Fast heterosynaptic learning in a robot food retrieval task inspired by the limbic system" @default.
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• W2143245633 doi "https://doi.org/10.1016/j.biosystems.2006.04.026" @default.
• W2143245633 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/17292537" @default.
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