All Outputs (6)

Neural fields with rebound currents: Novel routes to patterning (2021)
Journal Article
Modhara, S., Lai, Y. M., Thul, R., & Coombes, S. (2021). Neural fields with rebound currents: Novel routes to patterning. SIAM Journal on Applied Dynamical Systems, 20(3), 1596-1620. https://doi.org/10.1137/20M1364710

The understanding of how spatio-temporal patterns of neural activity may arise in the cortex of the brain has advanced with the development and analysis of neural field models. Replicating this success for subcortical tissues, such as the thalamus, r... Read More about Neural fields with rebound currents: Novel routes to patterning.

Interlayer connectivity reconstruction for multilayer brain networks using phase oscillator models (2021)
Journal Article
Tewarie, P., Prasse, B., Meier, J. M., Byrne, Á., De Domenico, M., Stam, C. J. (., …Van Mieghem, P. (2021). Interlayer connectivity reconstruction for multilayer brain networks using phase oscillator models. New Journal of Physics, 23(6), Article 063065. https://doi.org/10.1088/1367-2630/ac066d

Large-scale neurophysiological networks are often reconstructed from band-pass filtered time series derived from magnetoencephalography (MEG) data. Common practice is to reconstruct these networks separately for different frequency bands and to treat... Read More about Interlayer connectivity reconstruction for multilayer brain networks using phase oscillator models.

Mean-Field Models for EEG/MEG: From Oscillations to Waves (2021)
Journal Article
Byrne, Á., Ross, J., Nicks, R., & Coombes, S. (2022). Mean-Field Models for EEG/MEG: From Oscillations to Waves. Brain Topography, 35, 36–53. https://doi.org/10.1007/s10548-021-00842-4

Neural mass models have been used since the 1970s to model the coarse-grained activity of large populations of neurons. They have proven especially fruitful for understanding brain rhythms. However, although motivated by neurobiological consideration... Read More about Mean-Field Models for EEG/MEG: From Oscillations to Waves.

Understanding sensory induced hallucinations: From neural fields to amplitude equations (2021)
Journal Article
Nicks, R., Cocks, A., Avitabile, D., Johnston, A., & Coombes, S. (2021). Understanding sensory induced hallucinations: From neural fields to amplitude equations. SIAM Journal on Applied Dynamical Systems, 20(4), 1683-1714. https://doi.org/10.1137/20M1366885

Explorations of visual hallucinations, and in particular those of Billock and Tsou [V. A. Billock and B. H. Tsou, Proc. Natl. Acad. Sci. USA, 104 (2007), pp. 8490-8495], show that annular rings with a background flicker can induce visual hallucinatio... Read More about Understanding sensory induced hallucinations: From neural fields to amplitude equations.

Reinforcement Learning approaches to hippocampus-dependent flexible spatial navigation (2021)
Journal Article
Bast, T., Coombes, S., O’Dea, R., & Tessereau, C. (2021). Reinforcement Learning approaches to hippocampus-dependent flexible spatial navigation. Brain and Neuroscience Advances, 5, https://doi.org/10.1177/2398212820975634

Humans and non-human animals show great flexibility in spatial navigation, including the ability to return to specific locations based on as few as one single experience. To study spatial navigation in the laboratory, watermaze tasks, in which rats h... Read More about Reinforcement Learning approaches to hippocampus-dependent flexible spatial navigation.

Neural Fields with Rebound Currents: Novel Routes to Patterning (2021)
Journal Article
Modhara, S., Lai, Y. M., Thul, R., & Coombes, S. (2021). Neural Fields with Rebound Currents: Novel Routes to Patterning. SIAM Journal on Applied Dynamical Systems, 20(3), 1596-1620. https://doi.org/10.1137/20M1364710

The understanding of how spatio-temporal patterns of neural activity may arise in the cortex of the brain has advanced with the development and analysis of neural field models. To replicate this success for sub-cortical tissues, such as the thalamus,... Read More about Neural Fields with Rebound Currents: Novel Routes to Patterning.