David Sussillo
David Sussillo
Meta Reality Labs and Adjunct Professor @ Stanford
Verified email at
Cited by
Cited by
Context-dependent computation by recurrent dynamics in prefrontal cortex
V Mante, D Sussillo, KV Shenoy, WT Newsome
nature 503 (7474), 78, 2013
Generating coherent patterns of activity from chaotic neural networks
D Sussillo, LF Abbott
Neuron 63 (4), 544-557, 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
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
Opening the black box: low-dimensional dynamics in high-dimensional recurrent neural networks
D Sussillo, O Barak
Neural computation 25 (3), 626-649, 2013
Computation through neural population dynamics
S Vyas, MD Golub, D Sussillo, KV Shenoy
Annual review of neuroscience 43 (1), 249-275, 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
Feedforward inhibition contributes to the control of epileptiform propagation speed
AJ Trevelyan, D Sussillo, R Yuste
Journal of Neuroscience 27 (13), 3383-3387, 2007
Neural circuits as computational dynamical systems
D Sussillo
Current opinion in neurobiology 25, 156-163, 2014
Capacity and trainability in recurrent neural networks
J Collins, J Sohl-Dickstein, D Sussillo
arXiv preprint arXiv:1611.09913, 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
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
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
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
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
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
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
Random walks: Training very deep nonlin-ear feedforward networks with smart ini
D Sussillo
arXiv preprint arXiv 1412, 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
Hallucinations in neural machine translation
K Lee, O Firat, A Agarwal, C Fannjiang, D Sussillo
The system can't perform the operation now. Try again later.
Articles 1–20