Mean field theory for nonequilibrium network reconstruction.
about
A perspective on bridging scales and design of models using low-dimensional manifolds and data-driven model inferenceGibbs distribution analysis of temporal correlations structure in retina ganglion cells.The Ising decoder: reading out the activity of large neural ensembles.Stimulus-dependent maximum entropy models of neural population codes.Correlations and functional connections in a population of grid cells.Inferring synaptic structure in presence of neural interaction time scales.Scalability of Asynchronous Networks Is Limited by One-to-One Mapping between Effective Connectivity and CorrelationsApproximate Inference for Time-Varying Interactions and Macroscopic Dynamics of Neural Populations.A pairwise maximum entropy model accurately describes resting-state human brain networks.Differential Covariance: A New Class of Methods to Estimate Sparse Connectivity from Neural Recordings.Inference of the sparse kinetic Ising model using the decimation method.PRANAS: A New Platform for Retinal Analysis and Simulation.Bistability, non-ergodicity, and inhibition in pairwise maximum-entropy models.Dynamical maximum entropy approach to flocking.Learning and inference in a nonequilibrium Ising model with hidden nodes.Short-range interactions versus long-range correlations in bird flocks.Network inference from non-stationary spike trains.Neural network reconstruction using kinetic Ising models with memory.Inverse Ising problem in continuous time: A latent variable approach.Inferring hidden states in Langevin dynamics on large networks: Average case performance.Grid cells in an inhibitory network.Network inference in the nonequilibrium steady state.Maximum likelihood reconstruction for Ising models with asynchronous updates.Inference and learning in sparse systems with multiple states.Network inference using asynchronously updated kinetic Ising model.Functional coupling networks inferred from prefrontal cortex activity show experience-related effective plasticity.Inverse statistical problems: from the inverse Ising problem to data scienceFrom statistical inference to a differential learning rule for stochastic neural networks
P2860
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P2860
Mean field theory for nonequilibrium network reconstruction.
description
2011 nî lūn-bûn
@nan
2011年の論文
@ja
2011年学术文章
@wuu
2011年学术文章
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2011年学术文章
@zh-cn
2011年学术文章
@zh-hans
2011年学术文章
@zh-my
2011年学术文章
@zh-sg
2011年學術文章
@yue
2011年學術文章
@zh-hant
name
Mean field theory for nonequilibrium network reconstruction.
@en
Mean field theory for nonequilibrium network reconstruction.
@nl
type
label
Mean field theory for nonequilibrium network reconstruction.
@en
Mean field theory for nonequilibrium network reconstruction.
@nl
prefLabel
Mean field theory for nonequilibrium network reconstruction.
@en
Mean field theory for nonequilibrium network reconstruction.
@nl
P2860
P1476
Mean field theory for nonequilibrium network reconstruction.
@en
P2093
Yasser Roudi
P2860
P304
P356
10.1103/PHYSREVLETT.106.048702
P407
P577
2011-01-27T00:00:00Z