Abstract concept learning in a simple neural network inspired by the insect brain

Alex J. Cope*, Eleni Vasilaki, Dorian Minors, Chelsea Sabo, James A. R. Marshall, Andrew B. Barron

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)
27 Downloads (Pure)

Abstract

The capacity to learn abstract concepts such as ‘sameness’ and ‘difference’ is considered a higher-order cognitive function, typically thought to be dependent on top-down neocortical processing. It is therefore surprising that honey bees apparantly have this capacity. Here we report a model of the structures of the honey bee brain that can learn sameness and difference, as well as a range of complex and simple associative learning tasks. Our model is constrained by the known connections and properties of the mushroom body, including the protocerebral tract, and provides a good fit to the learning rates and performances of real bees in all tasks, including learning sameness and difference. The model proposes a novel mechanism for learning the abstract concepts of ‘sameness’ and ‘difference’ that is compatible with the insect brain, and is not dependent on top-down or executive control processing.

Original languageEnglish
Article numbere1006435
Pages (from-to)1-21
Number of pages21
JournalPLoS Computational Biology
Volume14
Issue number9
DOIs
Publication statusPublished - 17 Sep 2018

Bibliographical note

Copyright the Author(s) 2018. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

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