| A deep learning framework for neuroscience BA Richards, TP Lillicrap, P Beaudoin, Y Bengio, R Bogacz, ... Nature neuroscience 22 (11), 1761-1770, 2019 | 634 | 2019 |
| Convolutional neural networks as a model of the visual system: Past, present, and future GW Lindsay Journal of cognitive neuroscience 33 (10), 2017-2031, 2021 | 316 | 2021 |
| Parallel processing by cortical inhibition enables context-dependent behavior KV Kuchibhotla, JV Gill, GW Lindsay, ES Papadoyannis, RE Field, ... Nature Neuroscience 20 (1), 62-71, 2017 | 286 | 2017 |
| Attention in psychology, neuroscience, and machine learning GW Lindsay Frontiers in computational neuroscience 14, 29, 2020 | 142 | 2020 |
| How biological attention mechanisms improve task performance in a large-scale visual system model GW Lindsay, KD Miller eLife 7, e38105, 2018 | 70 | 2018 |
| Hebbian learning in a random network captures selectivity properties of the prefrontal cortex GW Lindsay, M Rigotti, MR Warden, EK Miller, S Fusi Journal of Neuroscience 37 (45), 11021-11036, 2017 | 42 | 2017 |
| 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 | 18 | 2021 |
| Models of the mind: how physics, engineering and mathematics have shaped our understanding of the brain G Lindsay Bloomsbury Publishing, 2021 | 18 | 2021 |
| Feature Based Attention in Convolutional Neural Networks GW Lindsay arXiv, 2015 | 18 | 2015 |
| A unified circuit model of attention: neural and behavioral effects GW Lindsay, DB Rubin, KD Miller bioRxiv, 2019.12. 13.875534, 2019 | 9* | 2019 |
| The neuroconnectionist research programme A Doerig, R Sommers, K Seeliger, B Richards, J Ismael, G Lindsay, ... arXiv preprint arXiv:2209.03718, 2022 | 4 | 2022 |
| Bio-inspired neural networks implement different recurrent visual processing strategies than task-trained ones do GW Lindsay, TD Mrsic-Flogel, M Sahani bioRxiv, 2022.03. 07.483196, 2022 | 4 | 2022 |
| Testing the tools of systems neuroscience on artificial neural networks GW Lindsay arXiv preprint arXiv:2202.07035, 2022 | 4 | 2022 |
| Divergent representations of ethological visual inputs emerge from supervised, unsupervised, and reinforcement learning GW Lindsay, J Merel, T Mrsic-Flogel, M Sahani arXiv preprint arXiv:2112.02027, 2021 | 4 | 2021 |
| Deep Convolutional Neural Networks as Models of the Visual System: Q&A. Neurdiness-Thinking about brains G Lindsay | 3 | 2018 |
| Recent Advances at the Interface of Neuroscience and Artificial Neural Networks Y Cohen, TA Engel, C Langdon, GW Lindsay, T Ott, MAK Peters, ... Journal of Neuroscience 42 (45), 8514-8523, 2022 | 2 | 2022 |
| Connecting scene statistics to probabilistic population codes and tuning properties of V1 neurons B Poole, I Lenz, G Lindsay, JM Samonds, TS Lee Soc Neurosci Abstr 36 (531.3), 2010 | 2 | 2010 |
| Understanding the functional and structural differences across excitatory and inhibitory neurons S Minni, L Ji-An, T Moskovitz, G Lindsay, K Miller, M Dipoppa, GR Yang bioRxiv, 680439, 2019 | 1 | 2019 |
| Do Biologically-Realistic Recurrent Architectures Produce Biologically-Realistic Models? GW Lindsay, TH Moskovitz, GR Yang, KD Miller Cognitive Computational Neuroscience, 2019 | 1 | 2019 |
| Membrane potential statistics reveal detailed correlation structure G Lindsay, AF Bujan, A Aertsen, A Kumar Front. Comput. Neurosci. Conference Abstract: Bernstein Conference, 2012 | 1 | 2012 |
Kenneth D. MillerProfessor of Neuroscience, Columbia UniversityVerified email at columbia.edu
