Skip to main content Skip to navigation


Get updates on new publications through my Google Scholar and Research Gate.


21. K. Pentland, M. Tamborrino, D. Samaddar, L. Appel (2021) Stochastic parareal: An application of probabilistic methods to time-parallelisation. arXiv:2106.10139.

    20. E. Buckwar, A. Samson, M. Tamborrino, I. Tubikanec (2021) Splitting methods for SDEs with locally Lipschitz drift. An illustration on the FitzHugh-Nagumo model. arXiv:2101.01027.


    19. I. Tubikanec, M. Tamborrino, P. Lansky, E. Buckwar. Qualitative properties of different numerical methods for the inhomogeneous geometric Brownian motion, J. Comput. Appl. Math., 406, 113951, 2022.

    18. M. Tamborrino, P. Lansky (2020) Shot noise, weak convergence and diffusion approximations. Physica D, 148, 132845, 2021. The R package for the exact simulation of shot noise processes, and non-Gaussian OU-processes (OU-Poisson, OU-Gamma, OU-Inverse Gaussian) is available on GitHub.

    17. M.J. Keeling, E. Hill, E. Gorsich, B. Penman, G. Guyver-Fletcher, A. Holmes, T. Leng, H. McKimm, M. Tamborrino, L. Dyson, M.Tildesley (2020) Predictions of COVID-19 dynamics in the UK: short-term forecasting and analysis of potential exit strategies. Plos Comput Biol. 171(1), e10086, 2021

      16. E. Buckwar, M. Tamborrino, I. Tubikanec. Spectral Density-Based and Measure-Preserving ABC for partially observed diffusion processes. A Demonstration on Hamiltonian SDEs, Stat. Comput., 30, 627-648, 2020. [Code].
      I spoke about this work at the Bernoulli-IMS One World Symposium 2020. My pre-recorded 10minutes talk is available here.

      15. G. D'Onofrio, P. Lansky, M. Tamborrino. Inhibition enhances the coherence in the Jacobi neuronal model, Chaos Sol. Fract., 128, 108-113, 2019.

      14. G. D'Onofrio, M. Tamborrino, P. Lansky. The Jacobi diffusion process as a neuronal model, Chaos 28, 103119, 1-10, 2018.

      13. M. Levakova, M. Tamborrino, L. Kostal and P. Lansky. Accuracy of rate coding: When shorter time window and higher spontaneous activity help. Phys. Rev. E, 95(2): 022310, 2017.

      12. M. Tamborrino, S. Ditlevsen, B. Markussen and S. Kyllingsb├Žk. Gaussian counter models for Visual Identification of Briefly Presented, Mutually Confusable Single Stimuli in Pure Accuracy Tasks. Math. Psychol., 79, 85-103, 2017.

      11.M. Levakova, M. Tamborrino, L. Kostal and P. Lansky. Presynaptic spontaneous activity enhances the accuracy of latency coding. Neural Comput., 28, 2162-2180, 2016.

      10. L. Sacerdote, M. Tamborrino and C. Zucca. First passage times of two-dimensional correlated processes: analytical results for the Wiener process and a numerical method for diffusion processes. J. Comput. Appl. Math., 296, 275-292, 2016.

      9 M. Tamborrino. Approximation of the first passage time density of a Brownian motion to an exponentially decaying threshold by two-piecewise linear threshold. Application to neuronal spiking activity. Math. Biosci. Eng., 13 (3), 613-629, 2016.

      8. J. C. Jacobsen, M. Tamborrino, P. Winkel, N. Haase, A. Perner, J. Wetterslev and C. Gluud. Count Data Analysis in Randomised Clinical Trials. J. Biom Biostat. 6, 1, 2015.

      7. M. Levakova, M. Tamborrino, S. Ditlevsen and P. Lansky. A review of the methods for neuronal response latency estimation. BioSystem, 136, 23-34, 2015

      6. M. Tamborrino, S. Ditlevsen and P. Lansky. Parametric inference from hitting times for perturbed Brownian motion. Lifetime Data Anal. 21 (3): 331-352, 2015.

      5. Tamborrino, L. Sacerdote and M. Jacobsen. Weak convergence of marked point processes generated by crossings of multivariate jump processes. Application to neural network modeling. Physica D, 288: 45-52, 2014.

      4. M. Tamborrino, S. Ditlevsen and P. Lansky. Parametric inference of neuronal response latency in presence of a background signal. {\em BioSystems}, 112: 249--257, 2013.

      3. M. Tamborrino, S. Ditlevsen and P. Lansky. Identification of noisy response latency. Phys. Rev. E, 86, 021128, 2012.

      2. L. Sacerdote, M. Tamborrino and C. Zucca. Detecting dependencies between spike trains of pairs of neurons through copulas. Brain Res., 1434: 243--256, 2012.

      1. L. Sacerdote and M. Tamborrino. Leaky Integrate and Fire models coupled through copulas: association properties of the Interspike Intervals. J. Physiol., 53 (6): 396-406, 2010.