Search results for "insect mushroom bodies"

showing 4 items of 4 documents

Motor-skill learning in an insect inspired neuro-computational control system

2017

In nature, insects show impressive adaptation and learning capabilities. The proposed computational model takes inspiration from specific structures of the insect brain: after proposing key hypotheses on the direct involvement of the mushroom bodies (MBs) and on their neural organization, we developed a new architecture for motor learning to be applied in insect-like walking robots. The proposed model is a nonlinear control system based on spiking neurons. MBs are modeled as a nonlinear recurrent spiking neural network (SNN) with novel characteristics, able to memorize time evolutions of key parameters of the neural motor controller, so that existing motor primitives can be improved. The ad…

0301 basic medicineComputer scienceBiomedical Engineeringinsect brainNonlinear controlAdaptation and learning03 medical and health sciences0302 clinical medicineMotor controllerArtificial Intelligenceinsect mushroom bodiesHypothesis and TheoryMotor skillSpiking neural networkHexapodgoal-oriented behaviorControl systemslearningbusiness.industryControl systems; Neural networks; Adaptation and learning030104 developmental biologyControl systemRobotArtificial intelligencespiking neural controllersMotor learningbusiness030217 neurology & neurosurgeryNeural networksNeuroscience
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A Fly-Inspired Mushroom Bodies Model for Sensory-Motor Control Through Sequence and Subsequence Learning

2016

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 n…

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 & neurosurgery
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Modeling the insect mushroom bodies: application to a delayed match-to-sample task.

2013

Despite their small brains, insects show advanced capabilities in learning and task solving. Flies, honeybees and ants are becoming a reference point in neuroscience and a main source of inspiration for autonomous robot design issues and control algorithms. In particular, honeybees demonstrate to be able to autonomously abstract complex associations and apply them in tasks involving different sensory modalities within the insect brain. Mushroom Bodies (MBs) are worthy of primary attention for understanding memory and learning functions in insects. In fact, even if their main role regards olfactory conditioning, they are involved in many behavioral achievements and learning capabilities, as …

Arthropod AntennaeInsectaComputer scienceCognitive Neurosciencemedia_common.quotation_subjectModels NeurologicalAction PotentialsInsectGrasshoppersOlfactory Receptor NeuronsTask (project management)03 medical and health sciences0302 clinical medicineStimulus modalityArtificial IntelligenceMemorymedicineLearningAnimalsComputer SimulationDrosophilaMushroom BodiesProblem Solving030304 developmental biologymedia_commonMatch-to-sample taskSpiking neural networkMotor Neurons0303 health sciencesArtificial neural networkbiologybusiness.industryInsect brain; Insect mushroom bodies; Learning; Neural model; Neuroscience; Spiking neurons; Action Potentials; Animals; Arthropod Antennae; Bees; Computer Simulation; Drosophila; Grasshoppers; Insecta; Memory; Motor Neurons; Mushroom Bodies; Nerve Net; Olfactory Receptor Neurons; Problem Solving; Artificial Intelligence; Models Neurological; Neural Networks ComputerBeesAutonomous robotbiology.organism_classificationInsect mushroom bodiesmedicine.anatomical_structureInsect brain; Insect mushroom bodies; LearningMushroom bodiesDrosophilaArtificial intelligenceNeural Networks ComputerNerve NetbusinessInsect brain030217 neurology & neurosurgeryNeuroanatomyNeural networks : the official journal of the International Neural Network Society
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Modelling the insect Mushroom Bodies: Application to sequence learning

2015

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 struc…

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 brainAlgorithmsNeural Networks
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