List of Publications

18. Hahn, G., Kumar, A., Schmidt, H., Knösche, T.R. and Deco, G., 2022. Rate and oscillatory switching dynamics of a multilayer visual microcircuit model. Elife, 11, p.e77594.

17. Schmidt, H. and R Knösche, T., 2022. Modelling the effect of ephaptic coupling on spike propagation in peripheral nerve fibres. Biological Cybernetics, pp.1-13.

16. Sánchez, S.M., Schmidt, H., Gallardo, G., Anwander, A., Brauer, J., Friederici, A.D. and Knösche, T.R., 2022. White matter brain structure predicts language performance and learning success. Human Brain Mapping.

15. Gast, R., Knösche, T.R. and Schmidt, H., 2021. Mean-field approximations of networks of spiking neurons with short-term synaptic plasticity. Physical Review E, 104(4), p.044310.

14. Gast, R., Gong, R., Schmidt, H., Meijer, H.G. and Knösche, T.R., 2021. On the role of arkypallidal and prototypical neurons for phase transitions in the external pallidum. Journal of neuroscience, 41(31), pp.6673-6683.

13. Schmidt, H., Hahn, G., Deco, G. and Knösche, T.R., 2021. Ephaptic coupling in white matter fibre bundles modulates axonal transmission delays. PLOS Computational Biology, 17(2), p.e1007858.

12. Gast, R., Schmidt, H. and Knösche, T.R., 2020. A mean-field description of bursting dynamics in spiking neural networks with short-term adaptation. Neural Computation, 32(9), pp.1615-1634.

11. Woldman, W., Schmidt, H., Abela, E., Chowdhury, F.A., Pawley, A.D., Jewell, S., Richardson, M.P. and Terry, J.R., 2020. Dynamic network properties of the interictal brain determine whether seizures appear focal or generalised. Scientific reports, 10(1), pp.1-11.

10. Schmidt, H. and Avitabile, D., 2020. Bumps and oscillons in networks of spiking neurons. Chaos: An Interdisciplinary Journal of Nonlinear Science, 30(3), p.033133.

9. Schmidt, H. and Knösche, T.R., 2019. Action potential propagation and synchronisation in myelinated axons. PLoS computational biology, 15(10), p.e1007004.

8. Schmidt, H., Avitabile, D., Montbrió, E. and Roxin, A., 2018. Network mechanisms underlying the role of oscillations in cognitive tasks. PLoS computational biology, 14(9), p.e1006430.

7. Schmidt, H., Woldman, W., Goodfellow, M., Chowdhury, F.A., Koutroumanidis, M., Jewell, S., Richardson, M.P. and Terry, J.R., 2016. A computational biomarker of idiopathic generalized epilepsy from resting state EEG. Epilepsia, 57(10), pp.e200-e204.

6. Avitabile, D. and Schmidt, H., 2015. Snakes and ladders in an inhomogeneous neural field model. Physica D: Nonlinear Phenomena, 294, pp.24-36.

5. Schmidt, H., Petkov, G., Richardson, M.P. and Terry, J.R., 2014. Dynamics on networks: the role of local dynamics and global networks on the emergence of hypersynchronous neural activity. PLoS computational biology, 10(11), p.e1003947.

4. Coombes, S., Schmidt, H. and Bojak, I., 2012. Interface dynamics in planar neural field models. The Journal of Mathematical Neuroscience, 2(1), pp.1-27.

3. Coombes, S., Schmidt, H., Laing, C.R., Svanstedt, N. and Wyller, J.A., 2012. Waves in random neural media. Discrete & Continuous Dynamical Systems, 32(8), p.2951.

2. Coombes, S. and Schmidt, H., 2010. Neural fields with sigmoidal firing rates: approximate solutions. Discrete and Continuous Dynamical Systems, 28(4), p.1369.

1. Schmidt, H., Hutt, A. and Schimansky-Geier, L., 2009. Wave fronts in inhomogeneous neural field models. Physica D: Nonlinear Phenomena, 238(14), pp.1101-1112.

Book Chapters

1. Coombes, S., Schmidt, H. and Avitabile, D., 2014. Spots: breathing, drifting and scattering in a neural field model. In Neural Fields (pp. 187-211). Springer, Berlin, Heidelberg.