David Sussillo, PhD

Affiliations: 
Electrical Engineering, Neurosciences Stanford University, Palo Alto, CA 
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"David Sussillo"
Mean distance: 14.7 (cluster 17)
 
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Publications

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Vyas S, Golub MD, Sussillo D, et al. (2020) Computation Through Neural Population Dynamics. Annual Review of Neuroscience. 43: 249-275
Maheswaranathan N, Williams AH, Golub MD, et al. (2019) Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics. Advances in Neural Information Processing Systems. 32: 15696-15705
Maheswaranathan N, Williams AH, Golub MD, et al. (2019) Universality and individuality in neural dynamics across large populations of recurrent networks. Advances in Neural Information Processing Systems. 2019: 15629-15641
Pandarinath C, O'Shea DJ, Collins J, et al. (2018) Inferring single-trial neural population dynamics using sequential auto-encoders. Nature Methods
Driscoll LN, Golub MD, Sussillo D. (2018) Computation through Cortical Dynamics. Neuron. 98: 873-875
Sussillo D, Stavisky SD, Kao JC, et al. (2017) Corrigendum: Making brain-machine interfaces robust to future neural variability. Nature Communications. 8: 14490
Sussillo D, Stavisky SD, Kao JC, et al. (2016) Making brain-machine interfaces robust to future neural variability. Nature Communications. 7: 13749
Kaufman MT, Seely JS, Sussillo D, et al. (2016) The Largest Response Component in the Motor Cortex Reflects Movement Timing but Not Movement Type. Eneuro. 3
Sussillo D, Churchland MM, Kaufman MT, et al. (2015) A neural network that finds a naturalistic solution for the production of muscle activity. Nature Neuroscience. 18: 1025-33
Sussillo D. (2014) Neural circuits as computational dynamical systems. Current Opinion in Neurobiology. 25: 156-63
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