Publications

T. Barta, L. Kostal (2024). Shared input and recurrency in neural networks for metabolically efficient information transmission. PLoS Comput. Biol..

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K. Rajdl, L. Kostal (2023). Estimation of the instantaneous spike train variability. Chaos Solit. Fractals.

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H. Lee, L. Kostal, R. Kanzaki, R. Kobayashi (2023). Spike frequency adaptation facilitates the encoding of input gradient in insect olfactory projection neurons. Biosystems.

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R. Tomar, L. Kostal (2021). Variability and Randomness of the Instantaneous Firing Rate. Front. Comput. Neurosci..

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T. Barta, L. Kostal (2021). Regular spiking in high conductance states: The essential role of inhibition. Phys. Rev. E.

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K. Rajdl, P. Lansky, L. Kostal (2020). Fano factor: a potentially useful information. Front. Comput. Neurosci..

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G. Ascione, G. D'Onofrio, L. Kostal, E. Pirozzi (2020). An optimal Gauss-Markov approximation for a process with stochastic drift and applications. Stoch. Proc. Appl..

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T. Barta, L. Kostal (2019). The effect of inhibition on rate code efficiency indicators. PLoS Comput. Biol..

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L. Kostal, R. Kobayashi (2019). Critical size of neural population for reliable information transmission. Phys. Rev. E (Rapid Commun.).

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M. Levakova, L. Kostal, C. Monsempès, P. Lucas, R. Kobayashi (2019). Adaptive integrate-and-fire model reproduces the dynamics of olfactory receptor neuron responses in moth. J. R. Soc. Interface.

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M. Levakova, L. Kostal, C. Monsempès, V. Jacob, P. Lucas (2018). Moth olfactory receptor neurons adjust their encoding efficiency to temporal statistics of pheromone fluctuations. PLoS Comput. Biol..

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L. Kostal, P. Lansky, M. Stiber (2018). Statistics of inverse interspike intervals: the instantaneous firing rate revisited. Chaos.

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L. Kostal, G. D'Onofrio (2018). Coordinate invariance as a fundamental constraint on the form of stimulus-specific information measures. Biol. Cybern..

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K. Rajdl, P. Lansky, L. Kostal (2017). Entropy Factor for Randomness Quantification in Neuronal Data. Neural Netw..

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M. Levakova, M. Tamborrino, L. Kostal, P. Lansky (2017). Accuracy of rate coding: When shorter time window and higher spontaneous activity help. Phys. Rev. E.

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M. Levakova, M. Tamborrino, L. Kostal, P. Lansky (2016). Presynaptic spontaneous activity enhances the accuracy of latency coding. Neural Comput..

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L. Kostal, S. Shinomoto (2016). Efficient information transfer by Poisson neurons. Math. Biosci. Eng..

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L. Kostal, P. Lansky (2016). Coding accuracy on the psychophysical scale. Sci. Rep..

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L. Kostal (2016). Stimulus reference frame and neural coding precision. J. Math. Psychol..

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L. Kostal, R. Kobayashi (2015). Optimal decoding and information transmission in Hodgkin-Huxley neurons under metabolic cost constraints. BioSystems.

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L. Kostal, P. Lansky, S. Pilarski (2015). Performance breakdown in optimal stimulus decoding. J. Neural Eng..

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L. Kostal, P. Lansky (2015). Coding accuracy is not fully determined by the neuronal model. Neural Comput..

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L. Kostal, S. Koyama (2014). The effect of interspike interval statistics on the information gain under the rate coding hypothesis. Math. Biosci. Eng..

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L. Kostal, P. Lansky (2013). Information capacity and its approximations under metabolic cost in a simple homogeneous population of neurons . BioSystems.

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L. Kostal, P. Lansky, M. D. McDonnell (2013). Metabolic cost of neuronal information in an empirical stimulus-response model. Biol. Cybern..

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L. Kostal, P. Lansky, O. Pokora (2013). Measures of statistical dispersion based on Shannon and Fisher information concepts. Inform. Sciences.

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L. Kostal, O. Pokora (2012). Nonparametric Estimation of Information-Based Measures of Statistical Dispersion. Entropy.

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L. Kostal, P. Lansky, O. Pokora (2012). How regular is neuronal activity?. ESANN 2012: The 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.

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L. Kostal (2012). Approximate information capacity of the perfect integrate-and-fire neuron using the temporal code. Brain Res..

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L. Kostal, P. Lansky, O. Pokora (2011). Variability measures of positive random variables. PLoS ONE.

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L. Kostal (2010). Information capacity in the weak-signal approximation. Phys. Rev. E.

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L. Kostal, P. Lansky (2010). Information transfer with small-amplitude signals. Phys. Rev. E (Rapid Commun.).

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L. Kostal, P. Marsalek (2010). Neuronal jitter: can we measure the spike timing dispersion differently?. Chin. J. Physiol..

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L. Kostal, P. Lansky (2008). Randomness of spontaneous activity and information transfer in neurons. Physiol. Res..

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L. Kostal, P. Lansky, J-P. Rospars (2008). Efficient olfactory coding in the pheromone receptor neuron of a moth. PLoS Comput. Biol..

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L. Kostal, P. Lansky, J-P. Rospars (2008). The Adaptation of the Moth Pheromone Receptor Neuron to its Natural Stimulus. AIP Conference Proceedings, Collective Dynamics: Topics on Competition and Cooperation in the Biosciences: A Selection of Papers in the Proceedings of the BIOCOMP2007 International Conference.

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L. Kostal, P. Lansky, J-P. Rospars (2007). Review: neuronal coding and spiking randomness. Eur. J. Neurosci..

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L. Kostal, P. Lansky, C. Zucca (2007). Randomness and variability of the neuronal activity described by the Ornstein-Uhlenbeck model. Netw. Comput. Neural Syst..

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L. Kostal, P. Lansky (2007). Variability and randomness in stationary neuronal activity. BioSystems.

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L. Kostal, P. Lansky, J-P. Rospars (2007). Encoding of pheromone intensity by dynamic activation of pheromone receptors. Neurocomputing.

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L. Kostal, P. Lansky (2006). Classification of stationary neuronal activity according to its information rate. Netw. Comput. Neural Syst..

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L. Kostal, P. Lansky (2006). Similarity of interspike interval distributions and information gain in a stationary neuronal firing. Biol. Cybern..

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P. Duchamp-Viret, L. Kostal, M. Chaput, P. Lansky, J-P. Rospars (2005). Patterns of spontaneous activity in single rat olfactory receptor neurons are different in normally breathing and tracheotomized animals. J. Neurobiol..

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