Search results for "Manifold"

showing 10 items of 415 documents

Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies

2022

In motor-related brain regions, movement intention has been successfully decoded from in-vivo spike train by isolating a lower-dimension manifold that the high-dimensional spiking activity is constrained to. The mechanism enforcing this constraint remains unclear, although it has been hypothesized to be implemented by the connectivity of the sampled neurons. We test this idea and explore the interactions between local synaptic connectivity and its ability to encode information in a lower dimensional manifold through simulations of a detailed microcircuit model with realistic sources of noise. We confirm that even in isolation such a model can encode the identity of different stimuli in a lo…

Computer and Information SciencesPhysiologyScienceModels NeurologicalInformation TheoryAction PotentialsNeurophysiologySynaptic TransmissionMembrane PotentialTopologyAnimal CellsClustering CoefficientsAnimalsManifoldsNeuronsMultidisciplinaryNeuronal MorphologyQuantitative Biology::Neurons and CognitionDirected GraphsvariabilityQRBiology and Life SciencesEigenvaluesSomatosensory CortexCell BiologyRatsMicrocircuitsElectrophysiologyAlgebraLinear AlgebraCellular NeuroscienceGraph TheoryPhysical SciencesEngineering and TechnologyMedicineCellular TypesdiverseMathematicsElectrical EngineeringResearch ArticleNeuroscienceElectrical Circuits
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Prediction of Hidden Oscillations Existence in Nonlinear Dynamical Systems: Analytics and Simulation

2013

From a computational point of view, in nonlinear dynamical systems, attractors can be regarded as self-excited and hidden attractors. Self-excited attractors can be localized numerically by a standard computational procedure, in which after a transient process a trajectory, starting from a point of unstable manifold in a neighborhood of equilibrium, reaches a state of oscillation, therefore one can easily identify it. In contrast, for a hidden attractor, a basin of attraction does not intersect neighborhoods of equilibria. While classical attractors are self-excited, attractors can therefore be obtained numerically by the standard computational procedure, for localization of hidden attracto…

Computer scienceOscillationbusiness.industryProcess (computing)State (functional analysis)Machine learningcomputer.software_genreManifoldNonlinear Sciences::Chaotic DynamicsAttractorTrajectoryPoint (geometry)Transient (oscillation)Artificial intelligenceStatistical physicsbusinesscomputer
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Manifold Learning with High Dimensional Model Representations

2020

Manifold learning methods are very efficient methods for hyperspectral image (HSI) analysis but, unless specifically designed, they cannot provide an explicit embedding map readily applicable to out-of-sample data. A common assumption to deal with the problem is that the transformation between the high input dimensional space and the (typically low) latent space is linear. This is a particularly strong assumption, especially when dealing with hyperspectral images due to the well-known nonlinear nature of the data. To address this problem, a manifold learning method based on High Dimensional Model Representation (HDMR) is proposed, which enables to present a nonlinear embedding function to p…

Computer sciencebusiness.industryNonlinear dimensionality reductionHyperspectral imaging020206 networking & telecommunicationsPattern recognition02 engineering and technologyFunction (mathematics)ManifoldNonlinear systemKernel (linear algebra)Transformation (function)0202 electrical engineering electronic engineering information engineeringEmbedding020201 artificial intelligence & image processingArtificial intelligencebusinessIGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium
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Topology guaranteeing manifold reconstruction using distance function to noisy data

2006

Given a smooth compact codimension one submanifold S of Rk and a compact approximation K of S, we prove that it is possible to reconstruct S and to approximate the medial axis of S with topological guarantees using unions of balls centered on K. We consider two notions of noisy-approximation that generalize sampling conditions introduced by Amenta & al. and Dey & al. Our results are based upon critical point theory for distance functions. For the two approximation conditions, we prove that the connected components of the boundary of unions of balls centered on K are isotopic to S. Our results allow to consider balls of different radii. For the first approximation condition, we also prove th…

Connected componentCombinatoricsCritical point (set theory)Medial axisHomotopyBoundary (topology)CodimensionSubmanifoldTopologyManifoldMathematicsProceedings of the twenty-second annual symposium on Computational geometry
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Separation properties of continuous maps in codimension 1 and geometrical applications

1992

Abstract Nuno Ballesteros, J.J. and M.C. Romero Fuster, Separation properties of continuous maps in codimension 1 and geometrical applications, Topology and its Applications 46 (1992) 107-111. We show that the image of a proper closed continuous map, f , from an n -manifold X to an ( n + 1)-manifold Y , such that H 1 (Y; Z 2 ) =0 , separates Y into at least two connected components provided the self-intersections set of f is not dense in any connected component of Y . We also obtain some geometrical applications.

Connected componentPure mathematicsContinuous mapImage (category theory)Alexander-Čech cohomology with compact supportCodimensionconvex curvesManifoldSet (abstract data type)Combinatoricsquasi-regular immersionsTangent developableGeometry and Topologyself-intersections setConnected componentstangent developableTopology (chemistry)MathematicsTopology and its Applications
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Fibre Bundle for Spin and Charge in General Relativity

2000

The Lorentzian and spin structures of general relativity are shown to allow a natural extension, by means of which the set of possible electromagnetic bundles is linked to the topology and geometry of the underlying causal structure. Further, both the Dirac operator and the electromagnetic potential are obtainable from a single linear connection 1-form.

Connection (fibred manifold)PhysicsGeneral relativityStatistical and Nonlinear PhysicsFour-forceDirac operatorMathematics of general relativitysymbols.namesakeTheory of relativityClassical mechanicssymbolsFiber bundleMathematical PhysicsCausal fermion systemCommunications in Mathematical Physics
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Graph matching for efficient classifiers adaptation

2011

In this work we present an adaptation algorithm focused on the description of the measurement changes under different acquisition conditions. The adaptation is carried out by transforming the manifold in the first observation conditions into the corresponding manifold in the second. The eventually non-linear transform is based on vector quantization and graph matching. The transfer learning mapping is defined in an unsupervised manner. Once this mapping has been defined, the labeled samples in the first are projected into the second domain, thus allowing the application of any classifier in the transformed domain. Experiments on VHR series of images show the validity of the proposed method …

Contextual image classificationbusiness.industryImage matchingVector quantizationVector quantisationPattern recognitionManifoldSupport vector machineLife ScienceArtificial intelligenceTransfer of learningbusinessClassifier (UML)Mathematics
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Editorial – About a ‚PLATO‘

2017

While learning has constantly been an object of research in manifold disciplines and fields, it has generally been understood in a positive sense. In the Age of Information, we are witnessing an increasing number of phenomena in the context of knowledge construction and accumulation that we describe as “negative learning”. This includes, for example, the deliberate circulation of counterfactual knowledge leading to negative learning outcomes, i.e., deficient decision-making and acting, like medical errors.

Counterfactual thinkingCognitive scienceInformation AgelawCirculation (currency)Context (language use)PsychologyManifold (fluid mechanics)Object (philosophy)law.invention
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Non-accumulation of critical points of the Poincaré time on hyperbolic polycycles

2007

We call Poincare time the time associated to the Poincar6 (or first return) map of a vector field. In this paper we prove the non-accumulation of isolated critical points of the Poincare time T on hyperbolic polycycles of polynomial vector fields. The result is obtained by proving that the Poincare time of a hyperbolic polycycle either has an unbounded principal part or is an almost regular function. The result relies heavily on the proof of Il'yashenko's theorem on non-accumulation of limit cycles on hyperbolic polycycles.

Critical period; finiteness; non-accumulation; quasi-analyticity; Dulac problem.Applied MathematicsGeneral MathematicsLimit cycleMathematical analysisHyperbolic manifoldPrincipal partUltraparallel theoremVector fieldRelatively hyperbolic groupCritical point (mathematics)Hyperbolic equilibrium pointMathematicsProceedings of the American Mathematical Society
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Crisis and credit: social and political challenges of the Malian tea market

2020

This article examines the manifold facets of crisis situations in Mali since the 1990s. Most prominent was la crise, the political crisis of 2012 that resulted from the rise of armed separatist gro...

Cultural Studies050204 development studiesPolitical crisis05 social sciences0507 social and economic geographyGreen tea050701 cultural studieslaw.inventionPoliticslawAnthropologyPolitical economyPolitical science0502 economics and businessManifold (fluid mechanics)African Identities
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