6533b7dbfe1ef96bd12714d9
RESEARCH PRODUCT
A Fly-Inspired Mushroom Bodies Model for Sensory-Motor Control Through Sequence and Subsequence Learning
Luca PatanéRoland StraussPaolo ArenaMarco CalíAgnese Porterasubject
Computer Networks and CommunicationsComputer scienceDecision MakingModels NeurologicalAction PotentialsContext (language use)Insect mushroom bodies bio-inspired control spiking neurons02 engineering and technologyVariation (game tree)Motor Activitybio-inspired control03 medical and health sciences0302 clinical medicineRewardSubsequence0202 electrical engineering electronic engineering information engineeringAnimalsLearningComputer SimulationMushroom BodiesTRACE (psycholinguistics)NeuronsSequencebio-inspired control; Insect mushroom bodies; learning; neural model; resonant neurons; spiking neurons; Action Potentials; Animals; Computer Simulation; Decision Making; Drosophila melanogaster; Learning; Motor Activity; Mushroom Bodies; Neurons; Perception; Reward; Robotics; Models Neurological; Neural Networks Computerspiking neuronsbusiness.industryRoboticsGeneral MedicineInsect mushroom bodiesComplex dynamicsDrosophila melanogasterMushroom bodiesPerception020201 artificial intelligence & image processingNeural Networks ComputerArtificial intelligenceSequence learningbusiness030217 neurology & neurosurgerydescription
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 | journal | country | edition | language |
|---|---|---|---|---|
| 2016-01-01 |