Title
A Fly-Inspired Mushroom Bodies Model For Sensory-Motor Control Through Sequence And Subsequence Learning
Abstract
Classification and sequence learning are relevant capabilities used by living beings to extract complex information from the environment for behavioral control. The insect world is full of examples where the presentation time of specific stimuli shapes the behavioral response. On the basis of previously developed neural models, inspired by Drosophila melanogaster, a new architecture for classification and sequence learning is here presented under the perspective of the Neural Reuse theory. Classification of relevant input stimuli is performed through resonant neurons, activated by the complex dynamics generated in a lattice of recurrent spiking neurons modeling the insect Mushroom Bodies neuropile. The network devoted to context formation is able to reconstruct the learned sequence and also to trace the subsequences present in the provided input. A sensitivity analysis to parameter variation and noise is reported. Experiments on a roving robot are reported to show the capabilities of the architecture used as a neural controller.
Year
DOI
Venue
2016
10.1142/S0129065716500350
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
Keywords
Field
DocType
Insect mushroom bodies, bio-inspired control, spiking neurons, learning, neural model, resonant neurons
Complex dynamics,Pattern recognition,Computer science,Artificial intelligence,Stimulus (physiology),Robot,Subsequence,Sequence learning,Perception,Machine learning,Robotics,Mushroom bodies
Journal
Volume
Issue
ISSN
26
6
0129-0657
Citations 
PageRank 
References 
3
0.55
27
Authors
5
Name
Order
Citations
PageRank
Paolo Arena126147.43
Marco Calí231.22
Luca Patané310417.31
Agnese Portera431.22
Roland Strauss5111.73