David Sussillo

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Krishna V ShenoyStanford and Howard Hughes Medical InstituteVerificeret mail på stanford.edu
L F AbbottBrandeis University, CERN, Boston University, Columbia University, Stanford Linear AcceleratorVerificeret mail på columbia.edu
Sergey D. StaviskyAssistant Professor, University of California, DavisVerificeret mail på ucdavis.edu
Jonathan KaoUniversity of California, Los AngelesVerificeret mail på seas.ucla.edu
Matthew GolubAssistant ProfessorVerificeret mail på cs.washington.edu
Valerio ManteUniversity of ZurichVerificeret mail på ini.uzh.ch
Matthew T KaufmanAssistant Professor at University of ChicagoVerificeret mail på uchicago.edu
Surya GanguliAssociate Professor, Stanford UniversityVerificeret mail på stanford.edu
Niru MaheswaranathanMeta Reality LabsVerificeret mail på fb.com
Mark ChurchlandProfessor of Neuroscience, Columbia UniversityVerificeret mail på columbia.edu
William T NewsomeStanford UniversityVerificeret mail på stanford.edu
Chethan PandarinathEmory University and Georgia Tech, Meta Reality LabsVerificeret mail på emory.edu
Jasmine CollinsResearch Scientist at Databricks/MosaicMLVerificeret mail på databricks.com
Jascha Sohl-DicksteinAnthropicVerificeret mail på anthropic.com
Omri BarakTechnion - Israel Institute of TechnologyVerificeret mail på tx.technion.ac.il
Laura DriscollStanford UniversityVerificeret mail på stanford.edu
Daniel YaminsAssociate Professor of Computer Science and of Psychology, Stanford UniversityVerificeret mail på stanford.edu
Aran NayebiAssistant Professor of Machine Learning, Carnegie Mellon University (CMU)Verificeret mail på cs.cmu.edu
Paul NuyujukianStanford UniversityVerificeret mail på stanford.edu
Jaimie M. HendersonProfessor of Neurosurgery, Stanford UniversityVerificeret mail på stanford.edu

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David Sussillo

Meta Reality Labs and Adjunct Professor @ Stanford

Verificeret mail på stanford.edu

Computational Neuroscience NeuroAI Dynamical Systems


Titel Sortér efter henvisninger Sortér efter årstal Sortér efter titel	Citeret af Citeret af	År
Context-dependent computation by recurrent dynamics in prefrontal cortex V Mante, D Sussillo, KV Shenoy, WT Newsome nature 503 (7474), 78, 2013	1824	2013
Generating coherent patterns of activity from chaotic neural networks D Sussillo, LF Abbott Neuron 63 (4), 544-557, 2009	1269	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	710	2018
Computation through neural population dynamics S Vyas, MD Golub, D Sussillo, KV Shenoy Annual review of neuroscience 43 (1), 249-275, 2020	619	2020
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	605	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	573	2013
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	403	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	308	2007
Neural circuits as computational dynamical systems D Sussillo Current opinion in neurobiology 25, 156-163, 2014	291	2014
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	254*	2023
Capacity and trainability in recurrent neural networks J Collins, J Sohl-Dickstein, D Sussillo arXiv preprint arXiv:1611.09913, 2016	241	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	226	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	221	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	208	2013
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	206	2016
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	186	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	178*	2016
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	162	2019
Random walks: Training very deep nonlin-ear feed-forward networks with smart ini D Sussillo arXiv preprint arXiv 1412, 2014	153*	2014
Hallucinations in neural machine translation K Lee, O Firat, A Agarwal, C Fannjiang, D Sussillo	148	2018

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