David Sussillo
David Sussillo
Senior Research Scientist @ Google Brain, Adjunct Professor @ Stanford
Verified email at google.com
Title
Cited by
Cited by
Year
Context-dependent computation by recurrent dynamics in prefrontal cortex
V Mante, D Sussillo, KV Shenoy, WT Newsome
nature 503 (7474), 78, 2013
7792013
Generating coherent patterns of activity from chaotic neural networks
D Sussillo, LF Abbott
Neuron 63 (4), 544-557, 2009
6582009
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
2892006
Opening the black box: low-dimensional dynamics in high-dimensional recurrent neural networks
D Sussillo, O Barak
Neural computation 25 (3), 626-649, 2013
2242013
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
2212015
Feedforward inhibition contributes to the control of epileptiform propagation speed
AJ Trevelyan, D Sussillo, R Yuste
Journal of Neuroscience 27 (13), 3383-3387, 2007
2032007
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
1382018
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
1202012
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
1152013
Neural circuits as computational dynamical systems
D Sussillo
Current opinion in neurobiology 25, 156-163, 2014
1132014
Capacity and trainability in recurrent neural networks
J Collins, J Sohl-Dickstein, D Sussillo
arXiv preprint arXiv:1611.09913, 2016
1102016
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, 5067-5075, 2016
95*2016
Spectrogram analysis of genomes
D Sussillo, A Kundaje, D Anastassiou
EURASIP Journal on Advances in Signal Processing 2004 (1), 790248, 2004
812004
Making brain–machine interfaces robust to future neural variability
D Sussillo, SD Stavisky, JC Kao, SI Ryu, KV Shenoy
Nature communications 7, 13749, 2016
762016
Information systems opportunities in brain–machine interface decoders
JC Kao, SD Stavisky, D Sussillo, P Nuyujukian, KV Shenoy
Proceedings of the IEEE 102 (5), 666-682, 2014
642014
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
542016
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, 5290-5301, 2018
512018
Self-tuning of neural circuits through short-term synaptic plasticity
D Sussillo, T Toyoizumi, W Maass
Journal of neurophysiology 97 (6), 4079-4095, 2007
472007
RANDOM WALKS: TRAINING VERY DEEP NONLIN-EAR FEED-FORWARD NETWORKS WITH SMART INI
D Sussillo
arXiv preprint arXiv 1412, 2014
44*2014
Lfads-latent factor analysis via dynamical systems
D Sussillo, R Jozefowicz, LF Abbott, C Pandarinath
arXiv preprint arXiv:1608.06315, 2016
412016
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