6533b821fe1ef96bd127ae2e
RESEARCH PRODUCT
Modelling the insect Mushroom Bodies: Application to sequence learning
Paolo ArenaAgnese PorteraMarco CalíLuca PatanéRoland Strausssubject
InsectaComputer scienceCognitive NeuroscienceModels NeurologicalContext; Insect brain; Insect mushroom bodies; Learning; Neural model; Neuroscience; Spiking neurons; Algorithms; Animals; Attention; Computer Simulation; Insecta; Mushroom Bodies; Robotics; Serial Learning; Models NeurologicalContext (language use)Sensory systemSerial LearningInsect brain; Insect mushroom bodies; LearningArtificial IntelligenceLearningAnimalsAttentionComputer SimulationMushroom BodiesStructure (mathematical logic)Sequencebusiness.industryRoboticsInsect mushroom bodiesMushroom bodiesSequence learningArtificial intelligencebusinessInsect brainAlgorithmsdescription
Learning and reproducing temporal sequences is a fundamental ability used by living beings to adapt behaviour repertoire to environmental constraints. This paper is focused on the description of a model based on spiking neurons, able to learn and autonomously generate a sequence of events. The neural architecture is inspired by the insect Mushroom Bodies (MBs) that are a crucial centre for multimodal sensory integration and behaviour modulation. The sequence learning capability coexists, within the insect brain computational model, with all the other features already addressed like attention, expectation, learning classification and others. This is a clear example that a unique neural structure is able to cope concurrently with a plethora of behaviours. Simulation results and robotic experiments are reported and discussed.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2015-01-01 | Neural Networks |