David Sussillo

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Krishna V ShenoyStanford and Howard Hughes Medical InstituteEmail verificata su stanford.edu
L F AbbottBrandeis University, CERN, Boston University, Columbia University, Stanford Linear AcceleratorEmail verificata su columbia.edu
Sergey D. StaviskyAssistant Professor, University of California, DavisEmail verificata su ucdavis.edu
Jonathan KaoUniversity of California, Los AngelesEmail verificata su seas.ucla.edu
Matthew GolubAssistant ProfessorEmail verificata su cs.washington.edu
Matthew T KaufmanAssistant Professor at University of ChicagoEmail verificata su uchicago.edu
Surya GanguliAssociate Professor, Stanford UniversityEmail verificata su stanford.edu
Valerio ManteUniversity of ZurichEmail verificata su ini.uzh.ch
Niru MaheswaranathanMeta Reality LabsEmail verificata su fb.com
Mark ChurchlandProfessor of Neuroscience, Columbia UniversityEmail verificata su columbia.edu
William T NewsomeStanford UniversityEmail verificata su stanford.edu
Jasmine CollinsResearch Scientist at Databricks/MosaicMLEmail verificata su databricks.com
Jascha Sohl-DicksteinAnthropicEmail verificata su anthropic.com
Chethan PandarinathEmory University and Georgia Tech, Meta Reality LabsEmail verificata su emory.edu
Omri BarakTechnion - Israel Institute of TechnologyEmail verificata su tx.technion.ac.il
Jonas KubiliusScientist in Residence @ Jones Lab / EMBLEmail verificata su gmc.vu.lt
James J DiCarloMassachusetts Institute of TechnologyEmail verificata su mit.edu
Daniel BearStanford UniversityEmail verificata su stanford.edu
Daniel YaminsAssociate Professor of Computer Science and of Psychology, Stanford UniversityEmail verificata su stanford.edu
Aran NayebiAssistant Professor of Machine Learning, Carnegie Mellon University (CMU)Email verificata su cs.cmu.edu

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David Sussillo

Meta Reality Labs and Adjunct Professor @ Stanford

Email verificata su stanford.edu

Computational Neuroscience NeuroAI Dynamical Systems


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	Citata da Citata da	Anno
Context-dependent computation by recurrent dynamics in prefrontal cortex V Mante, D Sussillo, KV Shenoy, WT Newsome nature 503 (7474), 78, 2013	1752	2013
Generating coherent patterns of activity from chaotic neural networks D Sussillo, LF Abbott Neuron 63 (4), 544-557, 2009	1213	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	666	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	578	2015
Computation through neural population dynamics S Vyas, MD Golub, D Sussillo, KV Shenoy Annual review of neuroscience 43 (1), 249-275, 2020	573	2020
Opening the black box: low-dimensional dynamics in high-dimensional recurrent neural networks D Sussillo, O Barak Neural computation 25 (3), 626-649, 2013	548	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	397	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	300	2007
Neural circuits as computational dynamical systems D Sussillo Current opinion in neurobiology 25, 156-163, 2014	285	2014
Capacity and trainability in recurrent neural networks J Collins, J Sohl-Dickstein, D Sussillo arXiv preprint arXiv:1611.09913, 2016	240	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	218	2012
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	212*	2023
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	202	2013
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	201	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	200	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	182	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	175*	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	154	2019
Random walks: Training very deep nonlin-ear feedforward networks with smart ini D Sussillo arXiv preprint arXiv 1412, 2014	152*	2014
Hallucinations in neural machine translation K Lee, O Firat, A Agarwal, C Fannjiang, D Sussillo	138	2018

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