DS
David Sussillo
cs.LGNeural Networks, Computerstat.MLcs.NEModels, Neurologicalneuroscienceq-bio.NCcs.AIcs.CLcs.CV
On Valency
published · living versionsW_gwnb3ukw·v1 · currentpublished
LFADS - Latent Factor Analysis via Dynamical Systems
with Rafal Jozefowicz, L. F. Abbott, Chethan Pandarinath
1 version
Preprints & journals
56 papers in the corpus · 2006–2026David Sussillo.41791374 · Sussillo, David2026 · 0 citationsNeuron. 2026;114(11):1893-1897
Computation-through-DynamicsToolkit: Simulated datasets and quality metrics for dynamical models of neural activity10.1101/2025.02.07.637062v3 · Versteeg, C., McCart, J. D., Ostrow, M. et al.2025 · 1 citationbioRxiv
Improved interpretability in LFADS models using a learned, context-dependent per-trial bias10.1101/2025.10.03.680303v1 · Shah, N. P., Abramovich Krasa, B., Kunz, E. et al.2025 · 0 citationsbioRxiv
Individual variability of neural computations underlying flexible decisions.39608399 · Pagan, Marino, Tang, Vincent D, Aoi, Mikio C et al.2025 · 50 citationsNature. 2025;639(8054):421-429
Universality and individuality in neural dynamics across large populations of recurrent networks.32782422 · Maheswaranathan, Niru, Williams, Alex H, Golub, Matthew D et al.2024 · 38 citationsAdvances in neural information processing systems. 2019;2019:15629-15641
A new theoretical framework jointly explains behavioral and neural variability across subjects performing flexible decision-making10.1101/2022.11.28.518207v2 · Pagan, M., Tang, V. D., Aoi, M. C. et al.2022 · 25 citationsbioRxiv
A generic noninvasive neuromotor interface for human-computer interaction10.1101/2024.02.23.581779v2 · Ctrl-labs at Reality Labs,, Sussillo, D., Kaifosh, P. et al.2024 · 42 citationsbioRxiv
Flexible multitask computation in recurrent networks utilizes shared dynamical motifs.38982201 · Driscoll, Laura N, Shenoy, Krishna, Sussillo, David2024 · 228 citationsNature neuroscience. 2024;27(7):1349-1363
Are task representations gated in macaque prefrontal cortex?2306.16733v1 · Timo Flesch, Valerio Mante, William Newsome et al.2023 · 2 citationsarXiv
Catalyzing next-generation Artificial Intelligence through NeuroAI.36949048 · Zador, Anthony, Escola, Sean, Richards, Blake et al.2023 · 305 citationsNature communications. 2023;14(1):1597
The centrality of population-level factors to network computation is demonstrated by a versatile approach for training spiking networks.36630961 · DePasquale, Brian, Sussillo, David, Abbott, L F et al.2023 · 64 citationsNeuron. 2023;111(5):631-649.e10
Analyzing Populations of Neural Networks via Dynamical Model Embedding2302.14078v1 · Jordan Cotler, Kai Sheng Tai, Felipe Hern'andez et al.2023 · 0 citationsarXiv
Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution2210.08340v3 · Anthony Zador, Sean Escola, Blake Richards et al.2022 · 33 citationsarXiv
One dimensional approximations of neuronal dynamics reveal computational strategy.36607933 · Brennan, Connor, Aggarwal, Adeeti, Pei, Rui et al.2023 · 16 citationsPLoS computational biology. 2023;19(1):e1010784
Cell-type-specific population dynamics of diverse reward computations.36113428 · Sylwestrak, Emily L, Jo, YoungJu, Vesuna, Sam et al.2022 · 81 citationsCell. 2022;185(19):3568-3587.e27
Flexible multitask computation in recurrent networks utilizes shared dynamical motifs10.1101/2022.08.15.503870v1 · Driscoll, L., Shenoy, K., Sussillo, D.2022 · 228 citationsbioRxiv
Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics.32782423 · Maheswaranathan, Niru, Williams, Alex H, Golub, Matthew D et al.2022 · 26 citationsAdvances in neural information processing systems. 2019;32:15696-15705
Recurrent Connections in the Primate Ventral Visual Stream Mediate a Trade-Off Between Task Performance and Network Size During Core Object Recognition.35798321 · Nayebi, Aran, Sagastuy-Brena, Javier, Bear, Daniel M et al.2022 · 32 citationsNeural computation. 2022;34(8):1652-1675
The geometry of integration in text classification RNNs2010.15114v2 · Kyle Aitken, Vinay V. Ramasesh, Ankush Garg et al.2020 · 4 citationsarXiv
Recurrent Connections in the Primate Ventral Visual Stream Mediate a Tradeoff Between Task Performance and Network Size During Core Object Recognition10.1101/2021.02.17.431717v3 · Nayebi, A., Sagastuy-Brena, J., Bear, D. M. et al.2021 · 34 citationsbioRxiv
Reverse engineering learned optimizers reveals known and novel mechanisms2011.02159v2 · Niru Maheswaranathan, David Sussillo, Luke Metz et al.2020 · 4 citationsarXiv
Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems2111.01256v1 · Jimmy T.H. Smith, Scott W. Linderman, David Sussillo2021 · 7 citationsarXiv
Computation Through Neural Population Dynamics.32640928 · Vyas, Saurabh, Golub, Matthew D, Sussillo, David et al.2021 · 750 citationsAnnual review of neuroscience. 2020;43:249-275
Corrigendum: Making brain-machine interfaces robust to future neural variability.28106034 · Sussillo, David, Stavisky, Sergey D, Kao, Jonathan C et al.2020 · 5 citationsNature communications. 2017;8:14490
How recurrent networks implement contextual processing in sentiment analysis2004.08013v1 · Niru Maheswaranathan, David Sussillo2020 · 21 citationsarXiv
Harnessing behavioral diversity to understand neural computations for cognition.31670073 · Musall, Simon, Urai, Anne E, Sussillo, David et al.2020 · 62 citationsCurrent opinion in neurobiology. 2019;58:229-238
Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics1906.10720v2 · Niru Maheswaranathan, Alex Williams, Matthew D. Golub et al.2019 · 61 citationsarXiv
Universality and individuality in neural dynamics across large populations of recurrent networks1907.08549v2 · Niru Maheswaranathan, Alex H. Williams, Matthew D. Golub et al.2019 · 38 citationsarXiv
Computation through Cortical Dynamics.29879388 · Driscoll, Laura N, Golub, Matthew D, Sussillo, David2019 · 11 citationsNeuron. 2018;98(5):873-875
Harnessing behavioral diversity to understand circuits for cognition1906.09622v1 · Simon Musall, Anne Urai, David Sussillo et al.2019 · 1 citationarXiv
Inferring single-trial neural population dynamics using sequential auto-encoders.30224673 · Pandarinath, Chethan, O'Shea, Daniel J, Collins, Jasmine et al.2019 · 804 citationsNature methods. 2018;15(10):805-815
Task-Driven Convolutional Recurrent Models of the Visual System1807.00053v2 · Aran Nayebi, Daniel Bear, Jonas Kubilius et al.2018 · 142 citationsarXiv
A Dataset and Architecture for Visual Reasoning with a Working Memory1803.06092v2 · Guangyu Robert Yang, Igor Ganichev, Xiao-Jing Wang et al.2018 · 46 citationsarXiv
Recurrent Segmentation for Variable Computational Budgets1711.10151v2 · Lane McIntosh, Niru Maheswaranathan, David Sussillo et al.2017 · 28 citationsarXiv
The Largest Response Component in the Motor Cortex Reflects Movement Timing but Not Movement Type.27761519 · Kaufman, Matthew T, Seely, Jeffrey S, Sussillo, David et al.2017 · 279 citationseNeuro. 2016;3(4)
Inferring single-trial neural population dynamics using sequential auto-encoders10.1101/152884v1 · Pandarinath, C., O'Shea, D. J., Collins, J. et al.2017 · 804 citationsbioRxiv
Input Switched Affine Networks: An RNN Architecture Designed for Interpretability1611.09434v2 · Jakob N. Foerster, Justin Gilmer, Jan Chorowski et al.2016 · 27 citationsarXiv
Capacity and Trainability in Recurrent Neural Networks1611.09913v3 · Jasmine Collins, Jascha Sohl-Dickstein, David Sussillo2016 · 79 citationsarXiv
Making brain-machine interfaces robust to future neural variability1610.05872v1 · David Sussillo, Sergey D. Stavisky, Jonathan C. Kao et al.2016 · 236 citationsNature Communications. 7:13749 (2016)on Valency
LFADS - Latent Factor Analysis via Dynamical Systems1608.06315v1 · David Sussillo, Rafal Jozefowicz, L. F. Abbott et al.2016 · 71 citationsarXivon Valency
A Neural Transducer1511.04868v4 · Navdeep Jaitly, David Sussillo, Quoc V. Le et al.2015 · 36 citationsarXiv
A neural network that finds a naturalistic solution for the production of muscle activity.26075643 · Sussillo, David, Churchland, Mark M, Kaufman, Matthew T et al.2015 · 683 citationsNature neuroscience. 2015;18(7):1025-33
Random Walk Initialization for Training Very Deep Feedforward Networks1412.6558v3 · David Sussillo, L.F. Abbott2014 · 68 citationsarXiv
Neural circuits as computational dynamical systems.24509098 · Sussillo, David2014 · 265 citationsCurrent opinion in neurobiology. 2014;25:156-63
Context-dependent computation by recurrent dynamics in prefrontal cortex.24201281 · Mante, Valerio, Sussillo, David, Shenoy, Krishna V et al.2013 · 2,128 citationsNature. 2013;503(7474):78-84
From fixed points to chaos: three models of delayed discrimination.23438479 · Barak, Omri, Sussillo, David, Romo, Ranulfo et al.2013 · 198 citationsProgress in neurobiology. 2013;103:214-22
Opening the black box: low-dimensional dynamics in high-dimensional recurrent neural networks.23272922 · Sussillo, David, Barak, Omri2013 · 613 citationsNeural computation. 2013;25(3):626-49
Transferring learning from external to internal weights in echo-state networks with sparse connectivity.22655041 · Sussillo, David, Abbott, L F2012 · 49 citationsPloS one. 2012;7(5):e37372
A recurrent neural network for closed-loop intracortical brain-machine interface decoders.22427488 · Sussillo, David, Nuyujukian, Paul, Fan, Joline M et al.2012 · 197 citationsJournal of neural engineering. 2012;9(2):026027
Generating coherent patterns of activity from chaotic neural networks.19709635 · Sussillo, David, Abbott, L F2009 · 1,180 citationsNeuron. 2009;63(4):544-57
Career total: 66 works. 56 are in this corpus.Showing the 50 most recent.
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