Search results for " learning"

showing 10 items of 5299 documents

Pheromone-induced olfactory memory in newborn rabbits: Involvement of consolidation and reconsolidation processes.

2009

Mammary pheromone (MP)-induced odor memory is a new model of appetitive memory functioning early in a mammal, the newborn rabbit. Some properties of this associative memory are analyzed by the use of anisomycin as an amnesic agent. Long-term memory (LTM) was impaired by anisomycin delivered immediately, but not 4 h after either acquisition or reactivation. Thus, the results suggest that this form of neonatal memory requires both consolidation and reconsolidation. By extending these notions to appetitive memory, the results reveal that consolidation and reconsolidation processes are characteristics of associative memories of positive events not only in the adult, but also in the newborn.

Cognitive NeuroscienceConditioning ClassicalPheromones03 medical and health sciencesCellular and Molecular Neurosciencechemistry.chemical_compound0302 clinical medicineAnimals[SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]Olfactory memoryAnisomycinComputingMilieux_MISCELLANEOUS030304 developmental biologyProtein Synthesis Inhibitors0303 health sciencesAppetitive BehaviorChi-Square DistributionConsolidation (soil)Long-term memoryAssociation LearningBrainRecognition PsychologyContent-addressable memoryOlfactory PerceptionNeuropsychology and Physiological PsychologyMemory Short-TermOdorchemistryAnimals NewbornPheromoneMemory consolidation[SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]RabbitsPsychologyNeuroscience030217 neurology & neurosurgeryAnisomycinCognitive psychologyLearningmemory (Cold Spring Harbor, N.Y.)
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Neural networks with non-uniform embedding and explicit validation phase to assess Granger causality

2015

A challenging problem when studying a dynamical system is to find the interdependencies among its individual components. Several algorithms have been proposed to detect directed dynamical influences between time series. Two of the most used approaches are a model-free one (transfer entropy) and a model-based one (Granger causality). Several pitfalls are related to the presence or absence of assumptions in modeling the relevant features of the data. We tried to overcome those pitfalls using a neural network approach in which a model is built without any a priori assumptions. In this sense this method can be seen as a bridge between model-free and model-based approaches. The experiments perfo…

Cognitive NeuroscienceEntropyFOS: Physical sciencesOverfittingcomputer.software_genreMachine learningGranger causalityArtificial IntelligenceMedicine and Health SciencesEntropy (information theory)Non-uniform embeddingComputer SimulationMathematicsArtificial neural networkbusiness.industryProbability and statisticsModels TheoreticalNeural Networks (Computer)ClassificationNeural networkAlgorithmCausalityPhysics - Data Analysis Statistics and ProbabilitySettore ING-INF/06 - Bioingegneria Elettronica E InformaticaGranger causalityEmbeddingA priori and a posterioriTransfer entropyNeural Networks ComputerArtificial intelligenceData miningbusinesscomputerAlgorithmsNeural networksData Analysis Statistics and Probability (physics.data-an)
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Active spike transmission in the neuron model with a winding threshold manifold

2012

International audience; We analyze spiking responses of excitable neuron model with a winding threshold manifold on a pulse stimulation. The model is stimulated with external pulse stimuli and can generate nonlinear integrate-and-fire and resonant responses typical for excitable neuronal cells (all-or-none). In addition we show that for certain parameter range there is a possibility to trigger a spiking sequence with a finite number of spikes (a spiking message) in the response on a short stimulus pulse. So active transformation of N incoming pulses to M (with M>N) outgoing spikes is possible. At the level of single neuron computations such property can provide an active "spike source" comp…

Cognitive Neuroscience[MATH.MATH-DS]Mathematics [math]/Dynamical Systems [math.DS][ MATH.MATH-DS ] Mathematics [math]/Dynamical Systems [math.DS][ NLIN.NLIN-CD ] Nonlinear Sciences [physics]/Chaotic Dynamics [nlin.CD]Threshold manifoldBiological neuron modelMachine learningcomputer.software_genreTopology01 natural sciences010305 fluids & plasmaslaw.inventionSpike encodingArtificial Intelligencelaw0103 physical sciences010306 general physicsSpike transmissionActive responseBifurcationMathematicsExcitabilityQuantitative Biology::Neurons and Cognitionbusiness.industry[SCCO.NEUR]Cognitive science/NeuroscienceDissipationComputer Science ApplicationsPulse (physics)[SPI.TRON]Engineering Sciences [physics]/Electronics[ SPI.TRON ] Engineering Sciences [physics]/ElectronicsNonlinear systemTransmission (telecommunications)Nonlinear dynamics[NLIN.NLIN-CD]Nonlinear Sciences [physics]/Chaotic Dynamics [nlin.CD][ SCCO.NEUR ] Cognitive science/NeuroscienceSpike (software development)Artificial intelligencebusinessManifold (fluid mechanics)computer
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Learning Motivation and Activity Contexts

1994

Abstract Learning motivation has a special explanatory status in educational psychology and educational practice. Motivation and learning often are studied separately. In the achievement motivation tradition, achievement situation is the connecting link between learning process and achievement need. The explanatory power of this link has limitations. The activity concept is proposed as a unit which is able to offer a broader basis for a unified concept of learning motivation.

Cognitive evaluation theoryConcept learningNeed for achievementMathematics educationEducational psychologyGoal theoryExplanatory powerPsychologyExperiential learningSocial psychologySelf-determination theoryEducationScandinavian Journal of Educational Research
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Contextual neural-network based spectrum prediction for cognitive radio

2015

Cognitive radio is the technique of effective electromagnetic spectrum usage important for future wireless communication including 5G networks. Neural networks are nature-inspired computational models used to solve cognitive radio prediction problems. This paper presents the use of contextual Sigma-if neural network in prediction of channel states for cognitive radio. Our results indicate that Sigma-if neural network confirms better predictions than Multilayer Perceptron (MLP) network and decreases sensing time for the benefit of the increase of the effectiveness of e-m spectrum usage.

Cognitive modelComputational modelArtificial neural networkspectrum sensingbusiness.industryTime delay neural networkComputer scienceComputer Science::Neural and Evolutionary Computationartificial intelligenceCognitive networkMachine learningcomputer.software_genrecontextual predictionCognitive radioMultilayer perceptron5G communicationcontextual processingWirelessArtificial intelligencebusinesscomputer2015 Fourth International Conference on Future Generation Communication Technology (FGCT)
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Why Untrained Control Groups Provide Invalid Baselines: A Reply to Dienes and Altmann

2003

Dienes and Altmann argue that an untrained control group provides a reliable baseline to measure artificial grammar learning. In this reply, we first provide a fictitious example to demonstrate that this assessment is faulty. We then analyse why this assessment is wrong, and we reiterate the solution proposed in Reber and Perruchet (this issue) for a proper control. Finally, we point out the importance of these methodological principles in the context of implicit learning studies. In their comment, Dienes and Altmann (this issue) raise two main concerns. First, they argue that any difference in classification between an experimental group and an untrained control group reflects the fact tha…

Cognitive scienceArtificial grammar learningPoint (typography)Grammarmedia_common.quotation_subject05 social sciences050109 social psychologyExperimental and Cognitive PsychologyContext (language use)Measure (mathematics)050105 experimental psychologyImplicit learningArgument0501 psychology and cognitive sciencesControl (linguistics)PsychologyGeneral Psychologymedia_commonThe Quarterly Journal of Experimental Psychology Section A
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Implicit learning, development, and education

2010

International audience; The present chapter focuses on implicit learning processes, and aims at showing that these processes could be used to design new methods of education or reeducation. After a brief definition of what we intend by implicit learning, we will show that these processes operate efficiently in development, from infancy to aging. Then, we will discuss the question of their resistance to neurological or psychiatric diseases. Finally, in a last section, we will comment on their potential use within an applied perspective.

Cognitive scienceComputer science4. Education05 social sciencesPerspective (graphical)Artificial GrammarExplicit LearningResistance (psychoanalysis)Open learningSerial Reaction Time TaskExperiential learning050105 experimental psychologyImplicit learning03 medical and health sciences0302 clinical medicineExplicit learningDevelopment (topology)[SCCO.PSYC]Cognitive science/Psychology0501 psychology and cognitive sciencesAmnesic PatientImplicit Learning030217 neurology & neurosurgery
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Adaptive and Generative Learning: Implications from Complexity Theories

2008

One of the most important classical typologies within the organizational learning literature is the distinction between adaptive and generative learning. However, the processes of these types of learning, particularly the latter, have not been widely analyzed and incorporated into the organizational learning process. This paper puts forward a new understanding of adaptive and generative learning within organizations, grounded in some ideas from complexity theories: mainly self-organization and implicate order. Adaptive learning involves any improvement or development of the explicate order through a process of self-organization. Self-organization is a self-referential process characterized …

Cognitive scienceCooperative learningbusiness.industryComputer scienceStrategy and ManagementAlgorithmic learning theoryGeneral Decision SciencesExperiential learningLearning sciencesGenerative modelManagement of Technology and InnovationOrganizational learningAdaptive learningbusinessAction learningInternational Journal of Management Reviews
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How does the brain encode epistemic reliability? Perceptual presence, phenomenal transparency, and counterfactual richness

2014

AbstractSeth develops a convincing and detailed internalist alternative to the sensorimotor-contingency theory of perceptual phenomenology. However, there are remaining conceptual problems due to a semantic ambiguity in the notion of “presence” and the idea of “subjective veridicality.” The current model should be integrated with the earlier idea that experiential “realness” and “mind-independence” are determined by the unavailability of earlier processing stages to attention. Counterfactual richness and attentional unavailability may both be indicators of the overall processing level currently achieved, a functional property that normally correlates with epistemic reliability. Perceptual p…

Cognitive scienceCounterfactual thinkingCognitive Neurosciencemedia_common.quotation_subjectInternalism and externalismAmbiguityENCODEExperiential learningEpistemologyPerceptionUnavailabilityPsychologyPhenomenology (psychology)media_commonCognitive psychologyCognitive Neuroscience
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Inside Self-Regulated Learning

2019

Cognitive scienceDevelopmental and Educational PsychologyPsychologySelf-regulated learningEducationZeitschrift für Entwicklungspsychologie und Pädagogische Psychologie
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