Attractor and integrator networks in the brain M Khona, IR Fiete Nature Reviews Neuroscience 23 (12), 744-766, 2022 | 150 | 2022 |
No free lunch from deep learning in neuroscience: A case study through models of the entorhinal-hippocampal circuit R Schaeffer, M Khona, I Fiete Advances in neural information processing systems 35, 16052-16067, 2022 | 54 | 2022 |
Reverse-engineering recurrent neural network solutions to a hierarchical inference task for mice R Schaeffer, M Khona, L Meshulam, IB Laboratory, IR Fiete Advances in Neural Information Processing Systems 33, 4584-4596, 2020 | 35 | 2020 |
Double descent demystified: Identifying, interpreting & ablating the sources of a deep learning puzzle R Schaeffer, M Khona, Z Robertson, A Boopathy, K Pistunova, JW Rocks, ... arXiv preprint arXiv:2303.14151, 2023 | 15* | 2023 |
Self-supervised learning of representations for space generates multi-modular grid cells R Schaeffer, M Khona, T Ma, C Eyzaguirre, S Koyejo, I Fiete Advances in Neural Information Processing Systems 36, 2024 | 11 | 2024 |
From smooth cortical gradients to discrete modules: spontaneous and topologically robust emergence of modularity in grid cells M Khona, S Chandra, IR Fiete bioRxiv, 2021.10. 28.466284, 2022 | 9* | 2022 |
Growing brains: Co-emergence of anatomical and functional modularity in recurrent neural networks Z Liu, M Khona, IR Fiete, M Tegmark arXiv preprint arXiv:2310.07711, 2023 | 7 | 2023 |
Winning the lottery with neural connectivity constraints: Faster learning across cognitive tasks with spatially constrained sparse rnns M Khona, S Chandra, JJ Ma, IR Fiete Neural Computation 35 (11), 1850-1869, 2023 | 6* | 2023 |
How capable can a transformer become? a study on synthetic, interpretable tasks R Ramesh, M Khona, RP Dick, H Tanaka, ES Lubana arXiv preprint arXiv:2311.12997, 2023 | 5 | 2023 |
Efficient online inference for nonparametric mixture models R Schaeffer, B Bordelon, M Khona, W Pan, IR Fiete Uncertainty in Artificial Intelligence, 2072-2081, 2021 | 4 | 2021 |
An Information-Theoretic Understanding of Maximum Manifold Capacity Representations B Isik, V Lecomte, R Schaeffer, Y LeCun, M Khona, R Shwartz-Ziv, ... UniReps: the First Workshop on Unifying Representations in Neural Models, 2023 | 3* | 2023 |
Large language models surpass human experts in predicting neuroscience results X Luo, A Rechardt, G Sun, KK Nejad, F Yáñez, B Yilmaz, K Lee, ... arXiv preprint arXiv:2403.03230, 2024 | 2 | 2024 |
Bridging Associative Memory and Probabilistic Modeling R Schaeffer, N Zahedi, M Khona, D Pai, S Truong, Y Du, M Ostrow, ... arXiv preprint arXiv:2402.10202, 2024 | 1* | 2024 |
Towards an Understanding of Stepwise Inference in Transformers: A Synthetic Graph Navigation Model M Khona, M Okawa, J Hula, R Ramesh, K Nishi, R Dick, ES Lubana, ... arXiv preprint arXiv:2402.07757, 2024 | 1 | 2024 |
Self-organized emergence of modularity, hierarchy, and mirror reversals from competitive synaptic growth in a developmental model of the visual pathway S Chandra, M Khona, T Konkle, I Fiete bioRxiv, 2024.01. 07.574543, 2024 | 1 | 2024 |
Caenorhabditis elegans foraging patterns follow a simple rule of thumb G Madirolas, A Al-Asmar, L Gaouar, L Marie-Louise, A Garza-Enríquez, ... Communications Biology 6 (1), 841, 2023 | 1 | 2023 |
In-Context Learning of Energy Functions R Schaeffer, M Khona, S Koyejo arXiv preprint arXiv:2406.12785, 2024 | | 2024 |
Towards an Improved Understanding and Utilization of Maximum Manifold Capacity Representations R Schaeffer, V Lecomte, DB Pai, A Carranza, B Isik, A Unell, M Khona, ... arXiv preprint arXiv:2406.09366, 2024 | | 2024 |
A morphing map model for place field organization in large environments M Naim, M Khona, CJ Cueva bioRxiv, 2024.04. 19.590254, 2024 | | 2024 |
See and Copy: Generation of complex compositional movements from modular and geometric RNN representations S Duan, M Khona, A Bertagnoli, S Chandra, I Fiete Proceedings of the 1st NeurIPS Workshop on Symmetry and Geometry in Neural …, 2023 | | 2023 |