Publications

Preprints

Published or accepted

20. Di Nardo E., D'Onofrio G., Martini T. (2024) Orthogonal gamma-based expansion for the CIR's first passage time distribution, Applied Mathematics and Computation, 480. View, arXiv

19. D'Onofrio G., Patie P., Sacerdote L. (2024) Jacobi processes with jumps as neuronal models: a first passage time analysis, SIAM Journal on Applied Mathematics 84(1):189-214. View, Read online

18. Ascione, G., D’Onofrio, G. (2023) Deterministic Control of SDEs with Stochastic Drift and Multiplicative Noise: A Variational Approach. Appl Math Optim 88, 11. View, Read online

17. D'Onofrio G. and Lanteri A., (2023) Approximating the First PassageTime Density of Diffusion Processes with State-Dependent Jumps. Fractal and Fractional, 7(1):30.  View

16. Di Nardo E., D'Onofrio G., Martini T., (2023) Approximating the first passage time density from data using generalized  Laguerre polynomials. Communications in Nonlinear Science and Numerical Simulation, 118, 106991.  View, Read online

15. Christodoulou C., D'Onofrio G., Stiber M. and Villa A.E.P., (2022) Editorial: Selected papers from the 14th international neural coding workshop, Seattle, Washington, Biosystems, 104818 View

14. Di Nardo E., D'Onofrio G., (2021) On the Cumulants of the First Passage Time of the Inhomogeneous Geometric Brownian Motion. Mathematics, 9(9):956.  View

13. Di Nardo E., D'Onofrio G., (2021), A cumulant approach for the first-passage-time problem of the Feller square-root process, Applied Mathematics and Computation, 391. View, Read online,  arXiv

12. G. Ascione, G. D’Onofrio, L. Kostal and E.Pirozzi, (2020), An optimal Gauss–Markov approximation for a process with stochastic drift and applications, Stochastic Processes and their Applications, 130(11), 6481-6514. View, Read online, arXiv

11. G. D'Onofrio, P. Lansky and M. Tamborrino, (2019), Inhibition enhances the coherence in the Jacobi neuronal model, Chaos, Solitons & Fractals, 128, 108-113. View, Read online , arXiv

10. G. D'Onofrio, M. Tamborrino and P. Lansky, (2018),  The Jacobi diffusion process as a neuronal model, Chaos, 28(10), 103119. View , Preprint

9. G.D'Onofrio, C.Macci and E.Pirozzi, (2018), Asymptotic results for first-passage times of some exponential processes, Methodology and Computing in Applied Probability, 20(4), 1453–1476. View  Read online

8. G.D'Onofrio, P.Lansky and E.Pirozzi, (2018), On two diffusion neuronal models with multiplicative noise: The mean first-passage time properties, Chaos, 28(4), 043103.   View

7. G.D'Onofrio, E.Pirozzi (2019), Asymptotics of Two-Boundary First-Exit-Time Densities for Gauss-Markov processes, Methodology and Computing in Applied Probability, 21(3), 735–752. View Read online

6. L.Kostal, G.D'Onofrio, (2018), Coordinate invariance as a fundamental constraint on the form of stimulus-specific information measures, Biological Cybernetics, 112(1), 13-23. View, preprint, Read online

5. G.D'Onofrio, E.Pirozzi, (2017),  Two-boundary first exit time of Gauss-Markov processes for stochastic modeling of acto-myosin dynamics, Journal of Mathematical Biology, 74(6), 1511–1531 . View

4. G.D’Onofrio, E.Pirozzi, (2016),  Successive Spike Times Predicted by a Stochastic Neuronal Model with a Variable Input Signal,  Mathematical Biosciences and Engineering, 13(3), 495-507. View

3. G.D’Onofrio, E.Pirozzi , (2015), On Two-Boundary First Exit Time of GaussDiffusion Processes: closed-form results and biological modeling, Lecture Notes of Seminario Interdisciplinare di Matematica, 12, 111-124.View

2. G.D’Onofrio, E.Pirozzi and M.O.Magnasco, (2015),Towards Stochastic Modeling of Neuronal Interspike Intervals Including a Time-Varying Input Signal , Lecture Notes in Computer Science, Springer-Verlag, vol 9520, 166-173. View

1. A.Buonocore, L.Caputo, G.D’Onofrio and E.Pirozzi,(2015), Closed-Form Solutions for the First-Passage-Time Problem and Neuronal Modeling, Ricerche di Matematica, 64(2), 421–439. DOI 10.1007/s11587-015-0248-6 View



Extended abstracts

1. G.D’Onofrio, E.Pirozzi, A Gauss-Markov based approach to model the neuronal firing activity in the presence of a time-varying threshold, (2014), 106–107.

2. G.D’Onofrio, E.Pirozzi, A Stochastic Model for Neuronal Firing Activity in the Presence of Time-Varying Input Signals. In: Computer Aided Systems Theory EUROCAST 2015 Extended Abstracts, 53–54 (2015) ISBN 978-84-606-5438-4 View

3. G.D’Onofrio, E.Pirozzi, Stochastic Modeling of Neuronal Firing Activity by Generalized Ornstein-Uhlenbeck Processes. CNS 2015 Extended Abstracts, (2015) View

PhD Thesis

G.D’Onofrio, Stochastic methods and models for neuronal activity and motor proteins (2016) View

Master Thesis

 G.D’Onofrio, Metodi e modelli di evoluzione stocastica (2012) (Italian) View 

In Italian

G.D’Onofrio, (2015), Dinamica del singolo neurone soggetto a rumore, Quaderni di Scienza e Scienziati Molisani 10(19),  115-123 (Italian)