| Improving deep learning with generic data augmentation L Taylor, G Nitschke 2018 IEEE Symposium Series on Computational Intelligence (SSCI), 1542-1547, 2018 | 980* | 2018 |
| Structural neuroimaging biomarkers for obsessive-compulsive disorder in the ENIGMA-OCD consortium: medication matters WB Bruin, L Taylor, RM Thomas, JP Shock, P Zhutovsky, Y Abe, P Alonso, ... Translational psychiatry 10 (1), 342, 2020 | 65 | 2020 |
| Hierarchical temporal prediction captures motion processing along the visual pathway Y Singer, L Taylor, BDB Willmore, AJ King, NS Harper Elife 12, e52599, 2023 | 17* | 2023 |
| Robust and accelerated single-spike spiking neural network training with applicability to challenging temporal tasks L Taylor, A King, N Harper arXiv preprint arXiv:2205.15286, 2022 | 7 | 2022 |
| A comparison between the Split Step Fourier and Finite-Difference method in analysing the soliton collision of a type of Nonlinear Schr\" odinger equation found in the context … L Taylor arXiv preprint arXiv:1709.04805, 2017 | 6 | 2017 |
| Addressing the speed-accuracy simulation trade-off for adaptive spiking neurons L Taylor, AJ King, NS Harper Thirty-seventh Conference on Neural Information Processing Systems, 2023 | 5 | 2023 |
| Temporal prediction captures key differences between spiking excitatory and inhibitory V1 neurons L Taylor, F Zenke, AJ King, NS Harper bioRxiv, 2024.05. 12.593763, 2024 | 1 | 2024 |
| Temporal prediction captures retinal spiking responses across animal species L Taylor, F Zenke, AJ King, NS Harper bioRxiv, 2024.03. 26.586771, 2024 | 1 | 2024 |
| Spike-to-excite: photosensitive seizures in biologically-realistic spiking neural networks L Taylor, MCM Fasol bioRxiv, 2024.08. 05.606699, 2024 | | 2024 |
| Advancing models of the visual system using biologically plausible unsupervised spiking neural networks L Taylor University of Oxford, 2023 | | 2023 |
| Accelerating spiking neural network training using the -block model L Taylor, AJ King, NS Harper | | |
