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

doi:10.1371/journal.pcbi.1006435

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

PLoS Comput Biol. 2018 Sep 17;14(9):e1006435. doi: 10.1371/journal.pcbi.1006435. eCollection 2018 Sep.

Authors

Alex J Cope^{1

2}, Eleni Vasilaki^{1

2}, Dorian Minors³, Chelsea Sabo^{1

2}, James A R Marshall^{1

2}, Andrew B Barron³

Affiliations

¹ Department of Computer Science, University of Sheffield, Sheffield, UK.
² Sheffield Robotics, University of Sheffield, Sheffield, UK.
³ Department of Biological Sciences, Macquarie University, Sydney, Australia.

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Animals
Bees / physiology*
Behavior, Animal
Brain / physiology*
Cognition*
Computer Simulation
Learning
Machine Learning
Models, Neurological
Mushroom Bodies / physiology*
Neural Networks, Computer*
Odorants
Probability
Software

Grants and funding

JARM and EV acknowledge support from the Engineering and Physical Sciences Research Council (grant numbers EP/J019534/1 and EP/P006094/1), (https://epsrc.ukri.org/). JARM and ABB acknowledge support from a Royal Society International Exchanges Grant (https://royalsociety.org/). ABB is supported by an Australian Research Council Future Fellowship Grant 140100452 and Australian Research Council Discovery Project Grant DP150101172 (http://www.arc.gov.au/). The funders played no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.