Reverse-engineering recurrent neural network solutions to a hierarchical inference task for mice. - Rylan Schaeffer; Mikail Khona; Leenoy Meshulam; International Brain Laboratory; Ila Fiete (NeurIPS, 2020)

Efficient online inference for nonparametric mixture models. - Rylan Schaeffer; Blake Bordelon; Mikail Khona; Weiwei Pan; Ila Rani Fiete (UAI, 2021)

See and Copy: Generation of complex compositional movements from modular and geometric RNN representations. - Sunny Duan; Mikail Khona; Adrian Bertagnoli; Sarthak Chandra; Ila Fiete (NeurReps, 2022)

No Free Lunch from Deep Learning in Neuroscience: A Case Study through Models of the Entorhinal-Hippocampal Circuit. - Rylan Schaeffer; Mikail Khona; Ila Fiete (NeurIPS, 2022)

Self-Supervised Learning of Representations for Space Generates Multi-Modular Grid Cells. - Rylan Schaeffer; Mikail Khona; Tzuhsuan Ma; Cristóbal Eyzaguirre; Sanmi Koyejo; Ila Fiete (NeurIPS, 2023)

Double Descent Demystified: Identifying, Interpreting & Ablating the Sources of a Deep Learning Puzzle. - Rylan Schaeffer; Mikail Khona; Zachary Robertson; Akhilan Boopathy; Kateryna Pistunova; Jason W. Rocks; Ila Rani Fiete; Oluwasanmi Koyejo (CoRR, 2023)

Growing Brains: Co-emergence of Anatomical and Functional Modularity in Recurrent Neural Networks. - Ziming Liu; Mikail Khona; Ila R. Fiete; Max Tegmark (CoRR, 2023)

How Capable Can a Transformer Become? A Study on Synthetic, Interpretable Tasks. - Rahul Ramesh; Mikail Khona; Robert P. Dick; Hidenori Tanaka; Ekdeep Singh Lubana (CoRR, 2023)

Winning the Lottery With Neural Connectivity Constraints: Faster Learning Across Cognitive Tasks With Spatially Constrained Sparse RNNs. - Mikail Khona; Sarthak Chandra; Joy J. Ma; Ila R. Fiete (Neural Comput., 2023)

Towards an Understanding of Stepwise Inference in Transformers: A Synthetic Graph Navigation Model. - Mikail Khona; Maya Okawa; Jan Hula; Rahul Ramesh; Kento Nishi; Robert P. Dick; Ekdeep Singh Lubana; Hidenori Tanaka (ICML, 2024)

Compositional Capabilities of Autoregressive Transformers: A Study on Synthetic, Interpretable Tasks. - Rahul Ramesh; Ekdeep Singh Lubana; Mikail Khona; Robert P. Dick; Hidenori Tanaka (ICML, 2024)

Bridging Associative Memory and Probabilistic Modeling. - Rylan Schaeffer; Nika Zahedi; Mikail Khona; Dhruv Pai; Sang T. Truong; Yilun Du; Mitchell Ostrow; Sarthak Chandra; Andres Carranza; Ila Rani Fiete; Andrey Gromov; Sanmi Koyejo (CoRR, 2024)

Large language models surpass human experts in predicting neuroscience results. - Xiaoliang Luo; Akilles Rechardt; Guangzhi Sun; Kevin K. Nejad; Felipe Yáñez; Bati Yilmaz; Kangjoo Lee; Alexandra O. Cohen; Valentina Borghesani; Anton Pashkov; Daniele Marinazzo; Jonathan Nicholas; et al. (CoRR, 2024)

Towards an Improved Understanding and Utilization of Maximum Manifold Capacity Representations. - Rylan Schaeffer; Victor Lecomte; Dhruv Bhandarkar Pai; Andres Carranza; Berivan Isik; Alyssa Unell; Mikail Khona; Thomas E. Yerxa; Yann LeCun; SueYeon Chung; Andrey Gromov; Ravid Shwartz-Ziv; et al. (CoRR, 2024)

In-Context Learning of Energy Functions. - Rylan Schaeffer; Mikail Khona; Sanmi Koyejo (CoRR, 2024)

Uncovering Latent Memories: Assessing Data Leakage and Memorization Patterns in Large Language Models. - Sunny Duan; Mikail Khona; Abhiram Iyer; Rylan Schaeffer; Ila R. Fiete (CoRR, 2024)

Representation Shattering in Transformers: A Synthetic Study with Knowledge Editing. - Kento Nishi; Maya Okawa; Rahul Ramesh; Mikail Khona; Ekdeep Singh Lubana; Hidenori Tanaka (CoRR, 2024)

Uncovering Latent Memories in Large Language Models. - Sunny Duan; Mikail Khona; Abhiram Iyer; Rylan Schaeffer; Ila R. Fiete (ICLR, 2025)