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

2018 ◽  
Vol 14 (9) ◽  
pp. e1006435 ◽  
Author(s):  
Alex J. Cope ◽  
Eleni Vasilaki ◽  
Dorian Minors ◽  
Chelsea Sabo ◽  
James A. R. Marshall ◽  
...  
2018 ◽  
Author(s):  
Alex J. Cope ◽  
Eleni Vasilaki ◽  
Dorian Minors ◽  
Chelsea Sabo ◽  
James A.R. Marshall ◽  
...  

AbstractThe 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.


2014 ◽  
Author(s):  
John Magnotti ◽  
Jeffrey Katz ◽  
Anthony Wright ◽  
Debbie Kelly

2010 ◽  
Author(s):  
Lucia Lazarowski ◽  
Rachel Eure ◽  
Mallory Gleason ◽  
Adam Goodman ◽  
Aly Mack ◽  
...  

2011 ◽  
Author(s):  
Marisa Hoeschele ◽  
Robert G. Cook ◽  
Lauren M. Guillette ◽  
Allison H. Hahn ◽  
Christopher B. Sturdy

2011 ◽  
Author(s):  
Thomas A. Daniel ◽  
Jeffrey S. Katz ◽  
Anthony A. Wright

2003 ◽  
Author(s):  
Jeffrey S. Katz ◽  
Kent D. Bodily ◽  
Michelle Hernandez ◽  
Anthony A. Wright

2003 ◽  
Author(s):  
Anthony A. Wright ◽  
Jeffrey S. Katz ◽  
Jacquelyne J. Rivera ◽  
Jocelyne Bachevalier

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Changyan Zhu ◽  
Eng Aik Chan ◽  
You Wang ◽  
Weina Peng ◽  
Ruixiang Guo ◽  
...  

AbstractMultimode fibers (MMFs) have the potential to carry complex images for endoscopy and related applications, but decoding the complex speckle patterns produced by mode-mixing and modal dispersion in MMFs is a serious challenge. Several groups have recently shown that convolutional neural networks (CNNs) can be trained to perform high-fidelity MMF image reconstruction. We find that a considerably simpler neural network architecture, the single hidden layer dense neural network, performs at least as well as previously-used CNNs in terms of image reconstruction fidelity, and is superior in terms of training time and computing resources required. The trained networks can accurately reconstruct MMF images collected over a week after the cessation of the training set, with the dense network performing as well as the CNN over the entire period.


Author(s):  
Fergyanto E. Gunawan ◽  
Herriyandi ◽  
Benfano Soewito ◽  
Tuga Mauritsius ◽  
Nico Surantha

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