Mattia Rigotti

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Stefano FusiColumbia UniversityVerified email at columbia.edu
Earl K. MillerPicower Professor of Neuroscience, Massachusetts Institute of TechnologyVerified email at mit.edu
Youssef MrouehPrincipal Research Scientist, IBM T.J Watson Research CenterVerified email at us.ibm.com
Omri BarakTechnion - Israel Institute of TechnologyVerified email at tx.technion.ac.il
Melissa R. WardenAssociate Professor of Translational Neuroscience, University of ArizonaVerified email at arizona.edu
Xiao-Jing WangGlobal Professor of Neural Science, New York UniversityVerified email at nyu.edu
Inkit PadhiIBM ResearchVerified email at ibm.com
Jérôme MunueraInserm, Paris Brain Institute (ICM), FranceVerified email at icm-institute.org
Silvia BernardiAssistant Professor of Psychiatry, Columbia UniversityVerified email at columbia.edu
Nathaniel D. DawHuo Professor of Computational and Theoretical Neuroscience, Princeton UniversityVerified email at princeton.edu
Daniel Ben Dayan RubinIntel LabsVerified email at intel.com
Joseph A. Gogos MD, PhDColumbia University, Zuckerman Institute and SNF Center for Precision Psychiatry and Mental HealthVerified email at columbia.edu
Joshua A. GordonDirector, National Institute of Mental HealthVerified email at nih.gov
Timothy SpellmanAssistant Professor of Neuroscience, University of Connecticut School of MedicineVerified email at med.cornell.edu
Susanne AhmariUniversity of PittsburghVerified email at upmc.edu
Eric Shea-BrownApplied Mathematics, University of WashingtonVerified email at uw.edu
Irina RishUniversity of Montreal / Mila -Quebec AI InstituteVerified email at mila.quebec
Brian KingsburyDistinguished Research Staff Member and Manager, IBM T. J. Watson Research Center, Yorktown HeightsVerified email at us.ibm.com
Srdjan OstojicEcole Normale Superieure ParisVerified email at ens.fr
Malte J RaschSony AI (previously: IBM Research AI)Verified email at sony.com

Mattia Rigotti

Researcher at IBM Research AI

Verified email at columbia.edu - Homepage

neuroscience neural networks deep learning machine learning computer vision


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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	1662	2013
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	710	2015
Why neurons mix: high dimensionality for higher cognition S Fusi, EK Miller, M Rigotti Current opinion in neurobiology 37, 66-74, 2016	675	2016
The geometry of abstraction in hippocampus and prefrontal cortex S Bernardi, MK Benna, M Rigotti, J Munuera, S Fusi, D Salzman	323	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	220	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	212	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	178	2015
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	104	2018
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	94	2021
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	83*	2019
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	72*	2021
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	59	2010
Attention-based interpretability with concept transformers M Rigotti, C Miksovic, I Giurgiu, T Gschwind, P Scotton International conference on learning representations, ICLR, 2021	53	2021
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	50	2017
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	49	2022
Energy-efficient neuromorphic classifiers D Marti, M Rigotti, M Seok, S Fusi Neural computation 28 (10), 2011-2044, 2016	43	2016
Ben Dayan Rubin D, Wang XJ, Fusi S M Rigotti Internal representation of task rules by recurrent dynamics: the importance …, 2010	40	2010
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	36	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	31	2022
Self-correcting Q-Learning R Zhu, M Rigotti Proceedings of the AAAI Conference on Artificial Intelligence 35 (12), 11185 …, 2020	23	2020

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