Smoothing of, and parameter estimation from, noisy biophysical recordings
about
Computational models in the age of large datasetsEstimating parameters and predicting membrane voltages with conductance-based neuron models.A unified approach to linking experimental, statistical and computational analysis of spike train data.Using waveform information in nonlinear data assimilation.A new look at state-space models for neural data.Assimilating seizure dynamicsA self-organizing state-space-model approach for parameter estimation in hodgkin-huxley-type models of single neurons.A continuous optimization approach for inferring parameters in mathematical models of regulatory networksA flexible, interactive software tool for fitting the parameters of neuronal models.BluePyOpt: Leveraging Open Source Software and Cloud Infrastructure to Optimise Model Parameters in Neuroscience.A state space approach for piecewise-linear recurrent neural networks for identifying computational dynamics from neural measurements.A sequential Monte Carlo approach to estimate biophysical neural models from spikes.Computational identification of receptive fields.Spike inference from calcium imaging using sequential Monte Carlo methods.Theory and simulation in neuroscience.Improving the detection sensitivity of chromatography by stochastic resonance.Beyond the connectome: the dynome.Efficient Markov chain Monte Carlo methods for decoding neural spike trains.Using computational theory to constrain statistical models of neural data.Computational implications of biophysical diversity and multiple timescales in neurons and synapses for circuit performance.Is realistic neuronal modeling realistic?Dynamical estimation of neuron and network properties II: Path integral Monte Carlo methods.Automated evolutionary optimization of ion channel conductances and kinetics in models of young and aged rhesus monkey pyramidal neurons.Inferring trial-to-trial excitatory and inhibitory synaptic inputs from membrane potential using Gaussian mixture Kalman filtering.Optimal experimental design for sampling voltage on dendritic trees in the low-SNR regime.Markov chain Monte Carlo approach to parameter estimation in the FitzHugh-Nagumo model.Inferring synaptic inputs given a noisy voltage trace via sequential Monte Carlo methods.Estimation of time-dependent input from neuronal membrane potential.Fast state-space methods for inferring dendritic synaptic connectivity.Improved dimensionally-reduced visual cortical network using stochastic noise modeling.Efficient fitting of conductance-based model neurons from somatic current clamp.Data Assimilation Methods for Neuronal State and Parameter Estimation
P2860
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P2860
Smoothing of, and parameter estimation from, noisy biophysical recordings
description
2009 nî lūn-bûn
@nan
2009 թուականի Մայիսին հրատարակուած գիտական յօդուած
@hyw
2009 թվականի մայիսին հրատարակված գիտական հոդված
@hy
2009年の論文
@ja
2009年論文
@yue
2009年論文
@zh-hant
2009年論文
@zh-hk
2009年論文
@zh-mo
2009年論文
@zh-tw
2009年论文
@wuu
name
Smoothing of, and parameter estimation from, noisy biophysical recordings
@ast
Smoothing of, and parameter estimation from, noisy biophysical recordings
@en
Smoothing of, and parameter estimation from, noisy biophysical recordings
@nl
type
label
Smoothing of, and parameter estimation from, noisy biophysical recordings
@ast
Smoothing of, and parameter estimation from, noisy biophysical recordings
@en
Smoothing of, and parameter estimation from, noisy biophysical recordings
@nl
prefLabel
Smoothing of, and parameter estimation from, noisy biophysical recordings
@ast
Smoothing of, and parameter estimation from, noisy biophysical recordings
@en
Smoothing of, and parameter estimation from, noisy biophysical recordings
@nl
P2860
P3181
P1476
Smoothing of, and parameter estimation from, noisy biophysical recordings
@en
P2093
Quentin J M Huys
P2860
P304
P3181
P356
10.1371/JOURNAL.PCBI.1000379
P407
P577
2009-05-01T00:00:00Z