Cascades and cognitive state: focused attention incurs subcritical dynamics ED Fagerholm, R Lorenz, G Scott, M Dinov, PJ Hellyer, N Mirzaei, ... Journal of Neuroscience 35 (11), 4626-4634, 2015 | 91 | 2015 |
The delayed response network: towards a single layer universal neural network approximator and delay-based learning D Martin, E Rut BMC Neuroscience 16 (Suppl 1), P214, 2015 | 26 | 2015 |
Modeling uncertainties in EEG microstates: Analysis of real and imagined motor movements using probabilistic clustering-driven training of probabilistic neural networks M Dinov, R Leech Frontiers in human neuroscience 11, 534, 2017 | 22 | 2017 |
Novel modeling of task vs. rest brain state predictability using a dynamic time warping spectrum: comparisons and contrasts with other standard measures of brain dynamics M Dinov, R Lorenz, G Scott, DJ Sharp, ED Fagerholm, R Leech Frontiers in computational neuroscience 10, 46, 2016 | 17 | 2016 |
The characteristic patterns of neuronal avalanches in mice under anesthesia and at rest: An investigation using constrained artificial neural networks ED Fagerholm, M Dinov, T Knöpfel, R Leech Plos one 13 (5), e0197893, 2018 | 10 | 2018 |
Using dynamic time warping for quantifying effects of sinusoidal oscillation deviations during EEG time series prediction and for finding interesting EEG and fMRI changes D Martin BMC Neuroscience 16 (Suppl 1), P63, 2015 | 3 | 2015 |
Deployment of access services based on HbbTV standard technology CA Martín, G Cisneros, JM Menéndez, P Orero, O Soler 2015 International Symposium on Consumer Electronics (ISCE), 1-2, 2015 | 3 | 2015 |
Tracking and optimizing human performance using deep reinforcement learning in closed-loop behavioral-and neuro-feedback: a proof of concept M Dinov, R Leech bioRxiv, 225995, 2017 | 2 | 2017 |
The design and implementation of novel computational and machine learning approaches for modelling brain dynamics: towards more interpretable and real-time brain analysis M Dinov Imperial College London, 2018 | | 2018 |
Neurofeedback training of large-scale brain networks R Lorenz, AA Faisal, M Dinov, IR Violante, R Leech | | 2014 |