| full-FORCE: A target-based method for training recurrent networks B DePasquale, CJ Cueva, K Rajan, GS Escola, LF Abbott PloS one 13 (2), e0191527, 2018 | 143 | 2018 |
| 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 | 107 | 2023 |
| Learning multiple variable-speed sequences in striatum via cortical tutoring JM Murray, GS Escola Elife 6, e26084, 2017 | 92 | 2017 |
| Hidden Markov models for the stimulus-response relationships of multistate neural systems S Escola, A Fontanini, D Katz, L Paninski Neural computation 23 (5), 1071-1132, 2011 | 79 | 2011 |
| Thalamic control of cortical dynamics in a model of flexible motor sequencing L Logiaco, LF Abbott, S Escola Cell reports 35 (9), 2021 | 70 | 2021 |
| Toward next-generation artificial intelligence: Catalyzing the neuroai revolution A Zador, S Escola, B Richards, B Ölveczky, Y Bengio, K Boahen, ... arXiv preprint arXiv:2210.08340, 2022 | 51 | 2022 |
| Reconstruction of Metabolic Networks from High‐Throughput Metabolite Profiling Data: In Silico Analysis of Red Blood Cell Metabolism I Nemenman, GS Escola, WS Hlavacek, PJ Unkefer, CJ Unkefer, ME Wall Annals of the New York Academy of Sciences 1115 (1), 102-115, 2007 | 37 | 2007 |
| Neuromatch Academy: Teaching computational neuroscience with global accessibility T van Viegen, A Akrami, K Bonnen, E DeWitt, A Hyafil, H Ledmyr, ... Trends in cognitive sciences 25 (7), 535-538, 2021 | 25 | 2021 |
| Remembrance of things practiced with fast and slow learning in cortical and subcortical pathways JM Murray, GS Escola Nature Communications 11 (1), 6441, 2020 | 22 | 2020 |
| A model of flexible motor sequencing through thalamic control of cortical dynamics L Logiaco, LF Abbott, S Escola Biorxiv, 2019.12. 17.880153, 2019 | 13 | 2019 |
| Dissociating the contributions of sensorimotor striatum to automatic and visually guided motor sequences KGC Mizes, J Lindsey, GS Escola, BP Ölveczky Nature Neuroscience 26 (10), 1791-1804, 2023 | 11 | 2023 |
| Maximally reliable Markov chains under energy constraints S Escola, M Eisele, K Miller, L Paninski Neural computation 21 (7), 1863-1912, 2009 | 9 | 2009 |
| Optimization and scaling of patient-derived brain organoids uncovers deep phenotypes of disease K Shah, R Bedi, A Rogozhnikov, P Ramkumar, Z Tong, B Rash, M Stanton, ... bioRxiv, 2020.08. 26.251611, 2020 | 8 | 2020 |
| Hidden Markov models applied toward the inference of neural states and the improved estimation of linear receptive fields S Escola, L Paninski COSYNE07, 2007 | 8 | 2007 |
| Thalamocortical motor circuit insights for more robust hierarchical control of complex sequences L Logiaco, GS Escola arXiv preprint arXiv:2006.13332, 2020 | 6 | 2020 |
| Motor cortex is required for flexible but not automatic motor sequences KGC Mizes, J Lindsey, GS Escola, BP Ölveczky bioRxiv, 2023 | 4 | 2023 |
| Specific connectivity optimizes learning in thalamocortical loops KJ Lakshminarasimhan, M Xie, JD Cohen, BA Sauerbrei, AW Hantman, ... Cell Reports 43 (4), 2024 | 3 | 2024 |
| Neuromatch Academy: a 3-week, online summer school in computational neuroscience BM t Hart, T Achakulvisut, A Adeyemi, A Akrami, B Alicea, ... Technological University Dublin, 2022 | 2 | 2022 |
| Hierarchical confounder discovery in the experiment-machine learning cycle A Rogozhnikov, P Ramkumar, R Bedi, S Kato, GS Escola Patterns 3 (4), 2022 | 1 | 2022 |
| Demuxalot: scaled up genetic demultiplexing for single-cell sequencing A Rogozhnikov, P Ramkumar, K Shah, R Bedi, S Kato, GS Escola bioRxiv, 2021.05. 22.443646, 2021 | 1 | 2021 |
L F AbbottBrandeis University, CERN, Boston University, Columbia University, Stanford Linear AcceleratorVerified email at columbia.edu
