| A deep learning framework for neuroscience BA Richards, TP Lillicrap, P Beaudoin, Y Bengio, R Bogacz, ... Nature neuroscience 22 (11), 1761-1770, 2019 | 996 | 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 | 618 | 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 | 363 | 2017 |
| Attention in psychology, neuroscience, and machine learning GW Lindsay Frontiers in computational neuroscience 14, 29, 2020 | 361 | 2020 |
| The neuroconnectionist research programme A Doerig, RP Sommers, K Seeliger, B Richards, J Ismael, GW Lindsay, ... Nature Reviews Neuroscience 24 (7), 431-450, 2023 | 165 | 2023 |
| Consciousness in artificial intelligence: insights from the science of consciousness P Butlin, R Long, E Elmoznino, Y Bengio, J Birch, A Constant, G Deane, ... arXiv preprint arXiv:2308.08708, 2023 | 157 | 2023 |
| How biological attention mechanisms improve task performance in a large-scale visual system model GW Lindsay, KD Miller eLife 7, e38105, 2018 | 102 | 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 | 55 | 2017 |
| Models of the mind: how physics, engineering and mathematics have shaped our understanding of the brain G Lindsay Bloomsbury Publishing, 2021 | 38 | 2021 |
| 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 | 33 | 2021 |
| 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 | 18 | 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 | 17 | 2022 |
| Feature Based Attention in Convolutional Neural Networks GW Lindsay arXiv, 2015 | 17 | 2015 |
| Testing methods of neural systems understanding GW Lindsay, D Bau Cognitive Systems Research 82, 101156, 2023 | 15 | 2023 |
| 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 | 11 | 2021 |
| A unified circuit model of attention: neural and behavioral effects GW Lindsay, DB Rubin, KD Miller bioRxiv, 2019.12. 13.875534, 2019 | 10* | 2019 |
| Grounding neuroscience in behavioral changes using artificial neural networks GW Lindsay Current opinion in neurobiology 84, 102816, 2024 | 9 | 2024 |
| Testing the tools of systems neuroscience on artificial neural networks GW Lindsay arXiv preprint arXiv:2202.07035, 2022 | 8 | 2022 |
| Deep neural networks are not a single hypothesis but a language for expressing computational hypotheses T Golan, JM Taylor, H Schütt, B Peters, RP Sommers, K Seeliger, ... Behavioral and Brain Sciences 46, 2023 | 7 | 2023 |
| Corrigendum: attention in psychology, neuroscience, and machine learning GW Lindsay Frontiers in Computational Neuroscience 15, 698574, 2021 | 5 | 2021 |
Kenneth D. MillerPeter Taylor Professor of Neuroscience, Columbia UniversityVahvistettu sähköpostiosoite verkkotunnuksessa columbia.edu
