DocumentCode :
2778342
Title :
Modeling attentional loop in the insect Mushroom Bodies
Author :
Arena, Paolo ; Patané, Luca ; Termini, Pietro Savio
Author_Institution :
Dipt. di Ing. Elettr., Elettron. e Inf., Univ. degli Studi di Catania, Catania, Italy
fYear :
2012
fDate :
10-15 June 2012
Firstpage :
1
Lastpage :
7
Abstract :
Insects show advanced capabilities and a rich behavioral repertoire in task solving and thus they are becoming a reference point in Neuroscience for studying simple cognitive structures. In particular, thanks to many neurogenetic tools, the fruit fly Drosophila melanogaster became a relevant source of inspiration for Robotics. Mushroom Bodies (MBs) are very interesting neural structures involved in the regulation of behaviors in fruit fly, even if their main role regards olfactory conditioning. In this paper a novel bio-inspired neural architecture is presented, where the MBs role in attention tasks is focused. The model is a multi-layer spiking neural network where the MBs and their direct and or indirect interactions to other key elements of the insect brain, the Central Complex and the Lateral Horns, are modeled. The biological background of the proposed model is presented together with a detailed description of the architecture; simulation results and remarks on the biological counterpart are also reported.
Keywords :
multilayer perceptrons; neural net architecture; robots; attentional loop modeling; behavioral repertoire; bioinspired neural architecture; biological background; central complex; cognitive structure; fruit fly Drosophila melanogaster; insect brain; insect mushroom body; lateral horn; multilayer spiking neural network; neural structure; neurogenetic tool; neuroscience; olfactory conditioning; robotics; task solving; Biological system modeling; Brain modeling; Insects; Lattices; Neurons; Visualization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks (IJCNN), The 2012 International Joint Conference on
Conference_Location :
Brisbane, QLD
ISSN :
2161-4393
Print_ISBN :
978-1-4673-1488-6
Electronic_ISBN :
2161-4393
Type :
conf
DOI :
10.1109/IJCNN.2012.6252833
Filename :
6252833
Link To Document :
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