Search results for "Neural"

showing 10 items of 2783 documents

Event-Based Trajectory Prediction Using Spiking Neural Networks

2021

International audience; In recent years, event-based sensors have been combined with spiking neural networks (SNNs) to create a new generation of bio-inspired artificial vision systems. These systems can process spatio-temporal data in real time, and are highly energy efficient. In this study, we used a new hybrid event-based camera in conjunction with a multi-layer spiking neural network trained with a spike-timing-dependent plasticity learning rule. We showed that neurons learn from repeated and correlated spatio-temporal patterns in an unsupervised way and become selective to motion features, such as direction and speed. This motion selectivity can then be used to predict ball trajectory…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]PolynomialComputer scienceNeuroscience (miscellaneous)Neurosciences. Biological psychiatry. Neuropsychiatry02 engineering and technologyunsupervised learningSNN[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]STDP03 medical and health sciencesCellular and Molecular Neuroscience0302 clinical medicineLearning rule0202 electrical engineering electronic engineering information engineeringEvent (probability theory)Original ResearchSpiking neural networkQuantitative Biology::Neurons and Cognitionmotion selectivitybusiness.industry[SCCO.NEUR]Cognitive science/Neuroscience[SCCO.NEUR] Cognitive science/NeuroscienceProcess (computing)Pattern recognitionspiking cameraTrajectoryball trajectory predictionUnsupervised learning020201 artificial intelligence & image processingArtificial intelligencebusiness030217 neurology & neurosurgeryEfficient energy useNeuroscienceRC321-571Frontiers in Computational Neuroscience
researchProduct

Optimisation et implémentation de méthodes bio-inspirées d'extraction de caractéristiques pour la reconnaissance d'objets visuels

2016

Industry has growing needs for so-called “intelligent systems”, capable of not only ac-quire data, but also to analyse it and to make decisions accordingly. Such systems areparticularly useful for video-surveillance, in which case alarms must be raised in case ofan intrusion. For cost saving and power consumption reasons, it is better to perform thatprocess as close to the sensor as possible. To address that issue, a promising approach isto use bio-inspired frameworks, which consist in applying computational biology modelsto industrial applications. The work carried out during that thesis consisted in select-ing bio-inspired feature extraction frameworks, and to optimize them with the aim t…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI][INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Bio-inspiréApprentissage automatiqueIntelligence artificielle[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]Descripteurs[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]EmbarquéAlgorithm-architecture matching[ INFO.INFO-BI ] Computer Science [cs]/Bioinformatics [q-bio.QM]Vision par ordinateurMachine learningRéseaux de neuronesComputer vision[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM][ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]OptimisationsFPGANeural networks[INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]
researchProduct

Deep learning for dehazing: Benchmark and analysis

2018

International audience; We compare a recent dehazing method based on deep learning , Dehazenet, with traditional state-of-the-art approach, on benchmark data with reference. Dehazenet estimates the depth map from a single color image, which is used to inverse the Koschmieder model of imaging in the presence of haze. In this sense, the solution is still attached to the Koschmieder model. We demonstrate that this method exhibits the same limitation than other inversions of this imaging model.

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI][INFO.INFO-MM] Computer Science [cs]/Multimedia [cs.MM][ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing[INFO.INFO-NE] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE][INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM][INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE][ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][STAT.ML] Statistics [stat]/Machine Learning [stat.ML][INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI][INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV][INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[STAT.ML]Statistics [stat]/Machine Learning [stat.ML][ INFO.INFO-NE ] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE][ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI][ STAT.ML ] Statistics [stat]/Machine Learning [stat.ML][ INFO.INFO-MM ] Computer Science [cs]/Multimedia [cs.MM]
researchProduct

Signal transformation from olfactory receptor neurons to central neurons

2010

International audience

[INFO.INFO-NE] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE][ INFO.INFO-NE ] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE][INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]ComputingMilieux_MISCELLANEOUS
researchProduct

Olfactory receptor neurons: A comparative analysis of their response properties with diverse stimuli in different species

2010

International audience

[INFO.INFO-NE] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE][ INFO.INFO-NE ] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE][INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]ComputingMilieux_MISCELLANEOUS
researchProduct

Analysis of the Signal Transformation From First- to Second-order Neurons in the Moth Sex-Pheromone Olfactory System

2010

International audience

[INFO.INFO-NE] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE][ INFO.INFO-NE ] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE][INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]ComputingMilieux_MISCELLANEOUS
researchProduct

High third and second order non linearities of chalcogenide glasses and fibers for compact infrared non linear devices.

2008

Due to their intrinsic nature, chalcogenide glasses present attractive nonlinearities from third and second order, with values reaching between 10 and 1000 times those of silica. We present a study of their properties and their shaping with the purpose to reach efficient devices in the near-mid infrared.

[PHYS.PHYS.PHYS-OPTICS] Physics [physics]/Physics [physics]/Optics [physics.optics]Materials scienceOptical fiberOptical glassChalcogenideInfraredPhysics::Optics02 engineering and technologyCondensed Matter::Disordered Systems and Neural Networks01 natural scienceslaw.invention010309 opticschemistry.chemical_compoundOpticslaw0103 physical sciencesComputingMilieux_MISCELLANEOUS[CHIM.MATE] Chemical Sciences/Material chemistry[PHYS.PHYS.PHYS-OPTICS]Physics [physics]/Physics [physics]/Optics [physics.optics][ PHYS.PHYS.PHYS-OPTICS ] Physics [physics]/Physics [physics]/Optics [physics.optics]business.industrySecond-harmonic generationOrder (ring theory)[CHIM.MATE]Chemical Sciences/Material chemistry021001 nanoscience & nanotechnologyNonlinear systemchemistry[ CHIM.MATE ] Chemical Sciences/Material chemistryOptoelectronics0210 nano-technologybusinessRefractive index
researchProduct

Nonlinear Sculpturing of Optical Pulses in Fibre Systems

2019

The interplay among the effects of dispersion, nonlinearity and gain/loss in optical fibre systems can be efficiently used to shape the pulses and manipulate and control the light dynamics and, hence, lead to different pulse-shaping regimes [1,2]. However, achieving a precise waveform with various prescribed characteristics is a complex issue that requires careful choice of the initial pulse conditions and system parameters. The general problem of optimisation towards a target operational regime in a complex multi-parameter space can be intelligently addressed by implementing machine-learning strategies. In this paper, we discuss a novel approach to the characterisation and optimisation of …

[PHYS.PHYS.PHYS-OPTICS] Physics [physics]/Physics [physics]/Optics [physics.optics][PHYS.PHYS.PHYS-OPTICS]Physics [physics]/Physics [physics]/Optics [physics.optics]Artificial neural networkComputer simulationComputer scienceData domain02 engineering and technology01 natural sciencesPulse shaping010309 opticsRange (mathematics)Nonlinear system020210 optoelectronics & photonicsControl theory0103 physical sciencesDispersion (optics)0202 electrical engineering electronic engineering information engineeringWaveformComputingMilieux_MISCELLANEOUS
researchProduct

Both the number and perceptual quality of odorants control configural processing of odor mixture in human adults

2014

Both the number and perceptual quality of odorants control configural processing of odor mixture in human adults. 36. annual meeting - association for chemoreception sciences (AChemS XXXVI)

[SDV.AEN] Life Sciences [q-bio]/Food and Nutrition[ SDV.AEN ] Life Sciences [q-bio]/Food and Nutritionmusculoskeletal neural and ocular physiology[SDV.AEN]Life Sciences [q-bio]/Food and Nutritionpsychological phenomena and processes
researchProduct

A biometic olfactory based biosensor combining electrochemistry and odorant-binding

2012

A biometic olfactory based biosensor combining electrochemistry and odorant-binding. Food Factory 2012

[SDV.AEN] Life Sciences [q-bio]/Food and Nutritionalimentationfood intakemusculoskeletal neural and ocular physiology[ SDV.AEN ] Life Sciences [q-bio]/Food and Nutritiondigestive oral and skin physiologytechnology industry and agriculturemacromolecular substances[SDV.AEN]Life Sciences [q-bio]/Food and Nutritionpsychological phenomena and processesolfaction
researchProduct