| Chromatin accessibility and guide sequence secondary structure affect CRISPR‐Cas9 gene editing efficiency KT Jensen, L Fløe, TS Petersen, J Huang, F Xu, L Bolund, Y Luo, L Lin FEBS letters 591 (13), 1892-1901, 2017 | 225 | 2017 |
| The neuroanatomical ultrastructure and function of a biological ring attractor DB Turner-Evans, KT Jensen, S Ali, T Paterson, A Sheridan, RP Ray, ... Neuron 108 (1), 145-163. e10, 2020 | 123 | 2020 |
| Natural continual learning: success is a journey, not (just) a destination TC Kao*, KT Jensen*, G van de Ven, A Bernacchia, G Hennequin Advances in Neural Information Processing Systems 34, 2021 | 43 | 2021 |
| Manifold GPLVMs for discovering non-Euclidean latent structure in neural data KT Jensen, TC Kao, M Tripodi, G Hennequin Advances in Neural Information Processing Systems 33, 2020 | 29 | 2020 |
| Long-term stability of single neuron activity in the motor system KT Jensen, N Kadmon Harpaz, AK Dhawale, SBE Wolff, BP Ölveczky Nature Neuroscience 25 (12), 1664-1674, 2022 | 27* | 2022 |
| Fusion of SpCas9 to E. coli Rec A protein enhances CRISPR-Cas9 mediated gene knockout in mammalian cells L Lin, TS Petersen, KT Jensen, L Bolund, R Kühn, Y Luo Journal of biotechnology 247, 42-49, 2017 | 25 | 2017 |
| Modeling Electron Transfers Using Quasidiabatic Hartree–Fock States KT Jensen, RL Benson, S Cardamone, AJW Thom Journal of Chemical Theory and Computation 14 (9), 4629-4639, 2018 | 23 | 2018 |
| iLQR-VAE: control-based learning of input-driven dynamics with applications to neural data M Schimel, TC Kao, KT Jensen, G Hennequin International Conference on Learning Representations (ICLR), 2022, 2022 | 16 | 2022 |
| Scalable Bayesian GPFA with automatic relevance determination and discrete noise models KT Jensen*, TC Kao*, JT Stone, G Hennequin Advances in Neural Information Processing Systems 34, 2021 | 16 | 2021 |
| A recurrent network model of planning explains hippocampal replay and human behavior KT Jensen, G Hennequin, MG Mattar Nature Neuroscience, 1-9, 2024 | 12 | 2024 |
| Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing Ensembles M Bjerke, L Schott, KT Jensen, C Battistin, DA Klindt, BA Dunn International Conference on Learning Representations (ICLR), 2023, 2023 | 4 | 2023 |
| An introduction to reinforcement learning for neuroscience KT Jensen arXiv preprint arXiv:2311.07315, 2023 | 2 | 2023 |
| Beyond the Euclidean brain: inferring non-Euclidean latent trajectories from spike trains KT Jensen, D Liu, TC Kao, M Lengyel, G Hennequin Cosyne Abstracts 2021, 2021 | 2 | 2021 |
| Strong and weak principles of Bayesian machine learning for systems neuroscience KT Jensen University of Cambridge, 2023 | 1 | 2023 |
| Some and Done? Temporally extended decisions with very few rollouts S Chen, KT Jensen, MG Mattar Proceedings of the Annual Meeting of the Cognitive Science Society 46, 2024 | | 2024 |
| A neural network model trained on free recall learns the method of loci M Li, KT Jensen, MG Mattar Proceedings of the Annual Meeting of the Cognitive Science Society 46, 2024 | | 2024 |
| Effects of noise and metabolic cost on cortical task representations JP Stroud, M Wójcik, KT Jensen, M Kusunoki, M Kadohisa, MJ Buckley, ... bioRxiv, 2023.07. 11.548492, 2023 | | 2023 |
Guillaume HennequinProfessor of Computational Neuroscience, University of Cambridge, UK在 cam.ac.uk 的电子邮件经过验证
