| The importance of mixed selectivity in complex cognitive tasks M Rigotti, O Barak, MR Warden, XJ Wang, ND Daw, EK Miller, S Fusi Nature 497 (7451), 585-590, 2013 | 1980 | 2013 |
| Why neurons mix: high dimensionality for higher cognition S Fusi, EK Miller, M Rigotti Current opinion in neurobiology 37, 66-74, 2016 | 880 | 2016 |
| Hippocampal–prefrontal input supports spatial encoding in working memory T Spellman, M Rigotti, SE Ahmari, S Fusi, JA Gogos, JA Gordon Nature 522 (7556), 309-314, 2015 | 823 | 2015 |
| The geometry of abstraction in hippocampus and prefrontal cortex S Bernardi, MK Benna, M Rigotti, J Munuera, S Fusi, D Salzman | 517 | 2018 |
| The sparseness of mixed selectivity neurons controls the generalization–discrimination trade-off O Barak, M Rigotti, S Fusi Journal of Neuroscience 33 (9), 3844-3856, 2013 | 270 | 2013 |
| Internal representation of task rules by recurrent dynamics: The importance of the diversity of neural responses M Rigotti, DBD Rubin, XJ Wang, S Fusi Frontiers in Computational Neuroscience 4, 24-29, 2010 | 239 | 2010 |
| Abstract context representations in primate amygdala and prefrontal cortex A Saez, M Rigotti, S Ostojic, S Fusi, CD Salzman Neuron 87 (4), 869-881, 2015 | 199 | 2015 |
| Tabular transformers for modeling multivariate time series I Padhi, Y Schiff, I Melnyk, M Rigotti, Y Mroueh, P Dognin, J Ross, R Nair, ... ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 156 | 2021 |
| Shared neural coding for social hierarchy and reward value in primate amygdala J Munuera, M Rigotti, CD Salzman Nature neuroscience 21 (3), 415-423, 2018 | 130 | 2018 |
| Predictive learning as a network mechanism for extracting low-dimensional latent space representations S Recanatesi, M Farrell, G Lajoie, S Deneve, M Rigotti, E Shea-Brown Nature communications 12 (1), 1417, 2021 | 118 | 2021 |
| Attention-based interpretability with concept transformers M Rigotti, C Miksovic, I Giurgiu, T Gschwind, P Scotton International conference on learning representations, ICLR, 2021 | 110 | 2021 |
| Mixed selectivity: Cellular computations for complexity KM Tye, EK Miller, FH Taschbach, MK Benna, M Rigotti, S Fusi Neuron 112 (14), 2289-2303, 2024 | 102 | 2024 |
| Beyond Backprop: Online Alternating Minimization with Auxiliary Variables A Choromanska, B Cowen, S Kumaravel, R Luss, M Rigotti, I Rish, ... International Conference on Machine Learning 97, 1193-1202, 2019 | 98* | 2019 |
| Image Captioning as an Assistive Technology: Lessons Learned from VizWiz 2020 Challenge P Dognin, I Melnyk, Y Mroueh, I Padhi, M Rigotti, J Ross, Y Schiff, ... Journal of Artificial Intelligence Research 73, 437-459, 2022 | 74 | 2022 |
| Compositional generalization through abstract representations in human and artificial neural networks T Ito, T Klinger, D Schultz, J Murray, M Cole, M Rigotti Advances in neural information processing systems 35, 32225-32239, 2022 | 68 | 2022 |
| The implications of categorical and category-free mixed selectivity on representational geometries MT Kaufman, MK Benna, M Rigotti, F Stefanini, S Fusi, AK Churchland Current opinion in neurobiology 77, 102644, 2022 | 66 | 2022 |
| Attractor concretion as a mechanism for the formation of context representations M Rigotti, D Ben Dayan Rubin, SE Morrison, CD Salzman, S Fusi Neuroimage 52 (3), 833-847, 2010 | 66 | 2010 |
| 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 | 60 | 2017 |
| Energy-efficient neuromorphic classifiers D Marti, M Rigotti, M Seok, S Fusi Neural computation 28 (10), 2011-2044, 2016 | 47 | 2016 |
| Ben Dayan Rubin D, Wang XJ, Fusi S M Rigotti Internal representation of task rules by recurrent dynamics: the importance …, 2010 | 43 | 2010 |
Stefano FusiColumbia UniversityVerified email at columbia.edu
