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PLoS Computational BiologyNeurosciencePeer-reviewed journal article

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

Alex J. CopeiD, Eleni VasilakiiD, Dorian MinorsiD, Chelsea M. SaboiD, James A. R. MarshalliD, Andrew B. BarroniD

PDFDOIPreprint

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 same-ness 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.

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