| Context-dependent computation by recurrent dynamics in prefrontal cortex V Mante, D Sussillo, KV Shenoy, WT Newsome nature 503 (7474), 78, 2013 | 1649 | 2013 |
| Generating coherent patterns of activity from chaotic neural networks D Sussillo, LF Abbott Neuron 63 (4), 544-557, 2009 | 1153 | 2009 |
| Inferring single-trial neural population dynamics using sequential auto-encoders C Pandarinath, DJ O’Shea, J Collins, R Jozefowicz, SD Stavisky, JC Kao, ... Nature methods 15 (10), 805-815, 2018 | 595 | 2018 |
| A neural network that finds a naturalistic solution for the production of muscle activity D Sussillo, MM Churchland, MT Kaufman, KV Shenoy Nature neuroscience 18 (7), 1025-1033, 2015 | 548 | 2015 |
| Opening the black box: low-dimensional dynamics in high-dimensional recurrent neural networks D Sussillo, O Barak Neural computation 25 (3), 626-649, 2013 | 509 | 2013 |
| Computation through neural population dynamics S Vyas, MD Golub, D Sussillo, KV Shenoy Annual review of neuroscience 43 (1), 249-275, 2020 | 487 | 2020 |
| Modular propagation of epileptiform activity: evidence for an inhibitory veto in neocortex AJ Trevelyan, D Sussillo, BO Watson, R Yuste The Journal of neuroscience 26 (48), 12447-12455, 2006 | 386 | 2006 |
| Feedforward inhibition contributes to the control of epileptiform propagation speed AJ Trevelyan, D Sussillo, R Yuste Journal of Neuroscience 27 (13), 3383-3387, 2007 | 290 | 2007 |
| Neural circuits as computational dynamical systems D Sussillo Current opinion in neurobiology 25, 156-163, 2014 | 261 | 2014 |
| Capacity and trainability in recurrent neural networks J Collins, J Sohl-Dickstein, D Sussillo arXiv preprint arXiv:1611.09913, 2016 | 231 | 2016 |
| A recurrent neural network for closed-loop intracortical brain–machine interface decoders D Sussillo, P Nuyujukian, JM Fan, JC Kao, SD Stavisky, S Ryu, K Shenoy Journal of neural engineering 9 (2), 026027, 2012 | 206 | 2012 |
| Making brain–machine interfaces robust to future neural variability D Sussillo, SD Stavisky, JC Kao, SI Ryu, KV Shenoy Nature communications 7 (1), 13749, 2016 | 194 | 2016 |
| The largest response component in the motor cortex reflects movement timing but not movement type MT Kaufman, JS Seely, D Sussillo, SI Ryu, KV Shenoy, MM Churchland eneuro 3 (4), 2016 | 194 | 2016 |
| From fixed points to chaos: three models of delayed discrimination O Barak, D Sussillo, R Romo, M Tsodyks, LF Abbott Progress in neurobiology 103, 214-222, 2013 | 192 | 2013 |
| Task-driven convolutional recurrent models of the visual system A Nayebi, D Bear, J Kubilius, K Kar, S Ganguli, D Sussillo, JJ DiCarlo, ... Advances in neural information processing systems 31, 2018 | 167 | 2018 |
| An online sequence-to-sequence model using partial conditioning N Jaitly, QV Le, O Vinyals, I Sutskever, D Sussillo, S Bengio Advances in neural information processing systems 29, 2016 | 167* | 2016 |
| Catalyzing next-generation artificial intelligence through neuroai A Zador, S Escola, B Richards, B Ölveczky, Y Bengio, K Boahen, ... Nature communications 14 (1), 1597, 2023 | 164* | 2023 |
| Random walks: Training very deep nonlin-ear feedforward networks with smart ini D Sussillo arXiv preprint arXiv 1412, 2014 | 146* | 2014 |
| Universality and individuality in neural dynamics across large populations of recurrent networks N Maheswaranathan, A Williams, M Golub, S Ganguli, D Sussillo Advances in neural information processing systems 32, 2019 | 133 | 2019 |
| Hallucinations in neural machine translation K Lee, O Firat, A Agarwal, C Fannjiang, D Sussillo | 127 | 2018 |
Krishna V ShenoyStanford and Howard Hughes Medical Institute在 stanford.edu 的电子邮件经过验证
