Search results for "insect brain"

showing 5 items of 5 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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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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Software/Hardware Issues in Modelling Insect Brain Architecture

2011

The concept of cognitive abilities is commonly associated to humans and animals like mammals, birds and others. Nevertheless, in the last years several research groups have intensified the studies on insects that posses a much simpler brain structure even if they are able to show interesting memory and learning capabilities. In this paper a survey on some key results obtained in a joint research activity among Engineers and Neurogeneticians is reported. They were focussed toward the design and implementation of a model of the insect brain inspired by the Drosophila melanogaster. Particular attention was paid to the main neural centers the Mushroom Bodies and the Central Complex. Moreover a …

melanogasterStructure (mathematical logic)Engineeringhybrid robotbusiness.industryController (computing)Insect brain; Drosophila; melanogaster; hybrid robot; dynamic simulationinsect brainCognitionDrosophila melanogasterSoftwareEmbodied cognitionKey (cryptography)RobotDrosophiladynamic simulationArchitecturebusinessComputer hardwareinsect brain; hybrid robot; Insect brain Drosophila melanogaster
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The Insect Mushroom Bodies: a Paradigm of Neural Reuse

2013

This paper is devoted to discuss the implementation of models,which are inspired by the fly Drosophila melanogaster and able to handle open problems in the field of robotics such as attention, expectation and sequence learning. The role of the Mushroom Bodies (MBs) in solving these tasks is analyzed in detail and a unifying plausible biologically inspired model is proposed. The developed neural structure is able to show different capabilities in line with the paradigm of neural reuse. The same neural circuit can be exploited to accomplish multiple tasks showing interesting capabilities such as attention, expectation and delayed match-to-sample. The simulation results here reported suggest a…

Structure (mathematical logic)Computer sciencebusiness.industryRoboticsinsect brainReuseMachine learningcomputer.software_genreField (computer science)Neural networks; insect brainBiological significanceMushroom bodiesArtificial intelligenceSequence learningbusinesscomputerNeural networks
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