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

Research output: Contribution to journalArticle

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.

LanguageEnglish
Article numbere1006435
Pages1-21
Number of pages21
JournalPLoS Computational Biology
Volume14
Issue number9
DOIs
StatePublished - 17 Sep 2018

Fingerprint

Concept Learning
neural networks
Insects
brain
Bees
Brain
learning
Learning
bee
Neural Networks
insect
Neural networks
insects
Honey
honey
honey bees
Mushroom Bodies
Processing
mushroom bodies
Learning Rate

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.

Cite this

Cope, A. J., Vasilaki, E., Minors, D., Sabo, C., Marshall, J. A. R., & Barron, A. B. (2018). Abstract concept learning in a simple neural network inspired by the insect brain. PLoS Computational Biology, 14(9), 1-21. [e1006435]. DOI: 10.1371/journal.pcbi.1006435
Cope, Alex J. ; Vasilaki, Eleni ; Minors, Dorian ; Sabo, Chelsea ; Marshall, James A. R. ; Barron, Andrew B./ Abstract concept learning in a simple neural network inspired by the insect brain. In: PLoS Computational Biology. 2018 ; Vol. 14, No. 9. pp. 1-21
@article{3a4ef8d35b8042b5b28729817915a8c9,
title = "Abstract concept learning in a simple neural network inspired by the insect brain",
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.",
author = "Cope, {Alex J.} and Eleni Vasilaki and Dorian Minors and Chelsea Sabo and Marshall, {James A. R.} and Barron, {Andrew B.}",
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.",
year = "2018",
month = "9",
day = "17",
doi = "10.1371/journal.pcbi.1006435",
language = "English",
volume = "14",
pages = "1--21",
journal = "PLoS Computational Biology",
issn = "1553-7358",
publisher = "Public Library of Science",
number = "9",

}

Cope, AJ, Vasilaki, E, Minors, D, Sabo, C, Marshall, JAR & Barron, AB 2018, 'Abstract concept learning in a simple neural network inspired by the insect brain' PLoS Computational Biology, vol 14, no. 9, e1006435, pp. 1-21. DOI: 10.1371/journal.pcbi.1006435

Abstract concept learning in a simple neural network inspired by the insect brain. / Cope, Alex J.; Vasilaki, Eleni; Minors, Dorian; Sabo, Chelsea; Marshall, James A. R.; Barron, Andrew B.

In: PLoS Computational Biology, Vol. 14, No. 9, e1006435, 17.09.2018, p. 1-21.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Abstract concept learning in a simple neural network inspired by the insect brain

AU - Cope,Alex J.

AU - Vasilaki,Eleni

AU - Minors,Dorian

AU - Sabo,Chelsea

AU - Marshall,James A. R.

AU - Barron,Andrew B.

N1 - 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.

PY - 2018/9/17

Y1 - 2018/9/17

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85054562908&partnerID=8YFLogxK

UR - http://purl.org/au-research/grants/arc/DP150101172

UR - http://purl.org/au-research/grants/arc/FT140100452

U2 - 10.1371/journal.pcbi.1006435

DO - 10.1371/journal.pcbi.1006435

M3 - Article

VL - 14

SP - 1

EP - 21

JO - PLoS Computational Biology

T2 - PLoS Computational Biology

JF - PLoS Computational Biology

SN - 1553-7358

IS - 9

M1 - e1006435

ER -

Cope AJ, Vasilaki E, Minors D, Sabo C, Marshall JAR, Barron AB. Abstract concept learning in a simple neural network inspired by the insect brain. PLoS Computational Biology. 2018 Sep 17;14(9):1-21. e1006435. Available from, DOI: 10.1371/journal.pcbi.1006435