Control theory, electrophysiology, and theoretical neuroscience.
T. B. Burghi, M. Ivanova, H. Wang, E. Marder, and T. O’Leary. “Rapid, interpretable, data-driven models of neural dynamics using Recurrent Mechanistic Models”. Proceedings of the National Academy of Sciences 122.32 (2025), e2426916122. doi: https://doi.org/10.1073/pnas.2426916122.
T. B. Burghi, K. Schapiro, M. Ivanova, H. Wang, E. Marder, and T. O’Leary. “Quantitative prediction of intracellular dynamics and synaptic currents in a small neural circuit”. Frontiers in Computational Neuroscience. in Computational Neuroscience 19 (2025). doi: https://doi.org/10.3389/fncom.2025.1515194.
T. B. Burghi and R. Sepulchre. “Adaptive Observers for Biophysical Neuronal Circuits”. IEEE Transactions on Automatic Control 69.8 (2024), pp. 5020–5033.
T. B. Burghi, J. G. Iossaqui, and J. F. Camino. “A General Update Rule for Lyapunov-Based Adaptive Control of Mobile Robots with Wheel Slip”. International Journal of Adaptive Control and Signal Processing 38.4 (2024), pp. 1172–1198.
T. Anttonen, T. B. Burghi, L. Duvall, M. P. Fernandez, G. Gutierrez, C. M. F. Kermen, and A. Michaiel. “Neurobiology and Changing Ecosystems: Mechanisms Underlying Responses to Human-Generated Environmental Impacts”. Journal of Neuroscience 43 (45 Nov. 2023).
C. Grussler, T. B. Burghi, and S. Sojoudi. “Internally Hankel k-Positive Systems”. SIAM Journal on Control and Optimization 60.4 (2022), pp. 2373–2392. doi: https://doi.org/10.1137/21M1404685.
R. Schmetterling, T. B. Burghi, and R. Sepulchre. “Adaptive conductance control”. Annual Reviews in Control 54 (2022), pp. 352–362. issn: 1367-5788. doi: https://doi.org/10.1016/j.arcontrol.2022.07.005.
C. Toschi, M. H. Sayed, T. B. Burghi, T. Sell, P. Moazen, L. Huang, U. Gether, T. W. Robbins, and J. W. Dalley. “Dissociating reward sensitivity and negative urgency effects on impulsivity in the five-choice serial reaction time task”. Brain and Neuroscience Advances 6 (2022), p. 23982128221102256. doi: https://doi.org/10.1177/23982128221102256.
T. B. Burghi, M. Schoukens, and R. Sepulchre. “Feedback identification of conductance-based models”. Automatica 123 (Jan. 2021), p. 109297. doi: https://doi.org/10.1016/j.automatica.2020.109297.
T. B. Burghi, G. P. Carratelli, M. E. Rule. “Costate control for nonlinear system identification with applications to excitable systems”. Accepted for publication in the Proceedings of the 65th IEEE Conference on Decision and Control (CDC). Honolulu, Hawaii, USA. Dec. 2026.
P. Parthasarathy, T. B. Burghi, T. O’Leary. Common Approaches to Spiking Neural Network Training Mask Adverse Sensitivity to Temporal Discretisation. Submitted to The 40th Annual Conference on Neural Information Processing Systems (Neurips 2026). Sidney, Australia, Dec. 2026. Under peer-review.
C. Grussler and T. B. Burghi. “On the monotonicity of frequency response gains”. In: Proceedings of the 62nd IEEE Conference on Decision and Control (CDC). Singapore, Singapore, Dec. 2023, pp. 1686–1691.
R. Schmetterling, T. B. Burghi, and R. Sepulchre. “Robust online estimation of biophysical neural circuits”. In: Proceedings of the 62nd IEEE Conference on Decision and Control (CDC). Singapore, Singapore, Dec. 2023, pp. 703–708.
T. B. Burghi, T. O’Leary, and R. Sepulchre. “Distributed online estimation of biophysical neural networks”. In: Proceedings of the 61st IEEE Conference on Decision and Control (CDC). Cancun, Mexico, Dec. 2022, pp. 628–634.
J. G. Lee, T. B. Burghi, and R. Sepulchre. “Open-loop contraction design”. In: Proceedings of the 61st IEEE Conference on Decision and Control (CDC). Cancun, Mexico, Dec. 2022, pp. 7339–7345.
J. G. Lee and T. B. Burghi. “Funnel control by induced contraction”. In: 25th International Symposium on Mathematical Theory of Networks and Systems (MTNS). Bayreuth, Germany, Sept. 2022, pp. 248–253.
T. B. Burghi, M. Schoukens, and R. Sepulchre. “System identification of biophysical neuronal models”. In: 59th IEEE Conference on Decision and Control. Jeju Island, South Korea, Dec. 2020, pp. 6180–6185.
T. B. Burghi, M. Schoukens, and R. Sepulchre. “Feedback for nonlinear system identification”. In: 18th European Control Conference (ECC). Naples, Italy, June 2019, pp. 1344–1349.
T. B. Burghi, J. G. Iossaqui, and J. F. Camino. “Stability analysis of perturbed nonlinear systems applied to the tracking control of a wheeled mobile robot under lateral slip”. In: XVII International Symposium on Dynamic Problems of Mechanics. Natal-RN, Brazil, Feb. 2015, pp. 22–27.
T. B. Burghi, Kyra Schapiro, Harry Wang, Eve Marder, Tim O’Leary. Closed-loop compensation of living neural rhythms. Biological control systems 2026, Oxford, United Kingdom. Sept. 2026.
T. B. Burghi, T. O’Leary, and R. Sepulchre. Neurons learn by predicting their synaptic inputs. Bernstein Computational Neuroscience Conference, Frankfurt am Main, Germany. Sept. 2024.
T. B. Burghi, E. Morozova, M. Ivanova, E. Marder, and T. O’Leary. Estimating mechanistic neuronal behaviour with artificial neural networks. Bernstein Computational Neuroscience Conference, Berlin, Germany. Sept. 2023.
E. Morozova, P. Newstein, T. B. Burghi, T. O’Leary, and E. Marder. Robustness of reciprocally inhibitory circuits to perturbation and neuromodulation. (Poster) Society for Neuroscience annual conference (SfN22), San Diego, USA. Nov. 2022.
T. B. Burghi, E. Morozova, S. Kedia, E. Marder, and T. O’Leary. Learning a biophysical neural model during the time frame of an experiment. FENS Forum 2022 (Federation of European Neuroscience Societies), Paris, France. Feb. 2022.
T. B. Burghi and R. Sepulchre. Identification of excitable neuronal systems. 27th Annual Computational Neuroscience (CNS) Meeting. Seattle, WA, United States, July 2018.