| Exploiting multiple timescales in hierarchical echo state networks L Manneschi, MOA Ellis, G Gigante, AC Lin, P Del Giudice, E Vasilaki Frontiers in Applied Mathematics and Statistics 6, 616658, 2021 | 24 | 2021 |
| SpaRCe: Improved learning of reservoir computing systems through sparse representations L Manneschi, AC Lin, E Vasilaki IEEE Transactions on Neural Networks and Learning Systems, 2021 | 18 | 2021 |
| A perspective on physical reservoir computing with nanomagnetic devices DA Allwood, MOA Ellis, D Griffin, TJ Hayward, L Manneschi, MF Musameh, ... Applied Physics Letters 122 (4), 2023 | 13 | 2023 |
| An alternative to backpropagation through time L Manneschi, E Vasilaki Nature Machine Intelligence 2 (3), 155-156, 2020 | 12 | 2020 |
| EchoVPR: Echo state networks for visual place recognition A Özdemir, M Scerri, AB Barron, A Philippides, M Mangan, E Vasilaki, ... IEEE Robotics and Automation Letters 7 (2), 4520-4527, 2022 | 7 | 2022 |
| Neuromorphic Few-Shot Learning: Generalization in Multilayer Physical Neural Networks KD Stenning, JC Gartside, L Manneschi, CTS Cheung, T Chen, ... | 7* | |
| Reconfigurable reservoir computing in a magnetic metamaterial IT Vidamour, C Swindells, G Venkat, L Manneschi, PW Fry, A Welbourne, ... Communications Physics 6 (1), 230, 2023 | 2 | 2023 |
| Signal neutrality, scalar property, and collapsing boundaries as consequences of a learned multi-timescale strategy L Manneschi, G Gigante, E Vasilaki, P Del Giudice PLoS Computational Biology 18 (8), e1009393, 2022 | 1 | 2022 |
| Deep Physical Reservoir Computing with Programmable Nanomagnetic Hierarchies K Stenning, J Gartside, L Manneschi, C Cheung, T Chen, J Love, ... Bulletin of the American Physical Society, 2023 | | 2023 |
| Efficient representations over multiple timescales L Manneschi University of Sheffield, 2022 | | 2022 |
