0000000000441599

AUTHOR

Pietro Savio Termini

showing 4 related works from this author

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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An insect brain computational model inspired by Drosophila melanogaster: architecture description

2010

The fruit fly Drosophila melanogaster is an extremely interesting insect because it shows a wealth of complex behaviors, despite its small brain. Nowadays genetic techniques allow to knock out the function of defined parts or genes in the Drosophila brain. Together with specific mutants which show similar defects in those parts or genes, hypothesis about the functions of every single brain part can be drawn. Following these experiments, a computational model of the fly Drosophila has been designed with a view to its robotic implementation.

biologymedia_common.quotation_subjectfungiSmall brainBrain PartComputational biologyInsectbiology.organism_classificationDrosophila melanogasterDrosophilaSoftware architecture descriptionFunction (biology)Cellular biophysicsmedia_common
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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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Visual learning in Drosophila: Application on a roving robot and comparisons

2011

Visual learning is an important aspect of fly life. Flies are able to extract visual cues from objects, like colors, vertical and horizontal distributedness, and others, that can be used for learning to associate a meaning to specific features (i.e. a reward or a punishment). Interesting biological experiments show trained stationary flying flies avoiding flying towards specific visual objects, appearing on the surrounding environment. Wild-type flies effectively learn to avoid those objects but this is not the case for the learning mutant rutabaga defective in the cyclic AMP dependent pathway for plasticity. A bio-inspired architecture has been proposed to model the fly behavior and experi…

genetic structuresPunishment (psychology)business.industryeducationfungiDrosophila; Hybrid robot; Spiking neurons; STDP; Visual cue-based navigationBiologySpiking neuronsbiology.organism_classificationSTDPVisual ObjectsHybrid robotRobotComputer visionDrosophilaArtificial intelligenceBiomimeticsVisual cue-based navigationbusinesscomputerDrosophilaVisual learningSensory cuecomputer.programming_language
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