0000000000384441

AUTHOR

Agnese Portera

showing 3 related works from this author

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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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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A Mushroom Bodies inspired spiking network for classification and sequence learning

2015

Sequence learning is a complex capability shown by living beings, able to extract information from the environment. Looking into the insect world, there are several examples where the presentation time of specific stimuli is considered to select the proper behavioural response. On the basis of previously developed neural models for sequence learning, inspired by the Drosophila melanogaster, a new formalization of key brain structures involved in the process is here provided. The input classification is performed through resonant neurons, stimulated by the complex dynamics generated in a lattice of recurrent spiking neurons modelling the Mushroom Bodies neuropile in the insect brain. The net…

SequenceBasis (linear algebra)Computer scienceProcess (engineering)business.industryContext (language use)Crystal latticesComplex dynamicsMushroom bodiesArtificial intelligenceSequence learningCrystal lattices; Filtration; Neural networksbusinessFiltrationNeural networksTRACE (psycholinguistics)Filtering; Insects; Lattices; Neurons
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