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Marie Levakova
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A review of the methods for neuronal response latency estimation
M Levakova, M Tamborrino, S Ditlevsen, P Lansky
Biosystems 136, 23-34, 2015
422015
Adaptive integrate-and-fire model reproduces the dynamics of olfactory receptor neuron responses in a moth
M Levakova, L Kostal, C Monsempès, P Lucas, R Kobayashi
Journal of the Royal Society Interface 16 (157), 20190246, 2019
202019
Moth olfactory receptor neurons adjust their encoding efficiency to temporal statistics of pheromone fluctuations
M Levakova, L Kostal, C Monsempès, V Jacob, P Lucas
PLoS computational biology 14 (11), e1006586, 2018
172018
Estimating latency from inhibitory input
M Levakova, S Ditlevsen, P Lansky
Biological cybernetics 108, 475-493, 2014
82014
Accuracy of rate coding: When shorter time window and higher spontaneous activity help
M Levakova, M Tamborrino, L Kostal, P Lansky
Physical Review E 95 (2), 022310, 2017
72017
Presynaptic spontaneous activity enhances the accuracy of latency coding
M Levakova, M Tamborrino, L Kostal, P Lansky
Neural Computation 28 (10), 2162-2180, 2016
62016
Effect of spontaneous activity on stimulus detection in a simple neuronal model
M Levakova
Mathematical Biosciences & Engineering 13 (3), 551-568, 2015
52015
Classification of brain states that predicts future performance in visual tasks based on co-integration analysis of EEG data
M Levakova, JH Christensen, S Ditlevsen
Royal Society Open Science 9 (11), 220621, 2022
32022
Penalisation Methods in Fitting High-Dimensional Cointegrated Vector Autoregressive Models: A Review
M Levakova, S Ditlevsen
International Statistical Review, 2023
12023
Efficiency of rate and latency coding with respect to metabolic cost and time
M Levakova
Biosystems 161, 31-40, 2017
2017
Neuronal response latency in the presence of spontaneous activity
M Leváková
2016
Cointegration analysis of EEG signals
M Levakova
2nd Annual Meeting of the NeuroMod Institute, 16, 0
Stochastic models and statistical analysis of series of events
M Leváková
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Artikler 1–13