full-FORCE: A Target-Based Method for Training Recurrent Networks. - Brian DePasquale; Christopher J. Cueva; Kanaka Rajan; G. Sean Escola; L. F. Abbott (CoRR, 2017)

Emergence of grid-like representations by training recurrent neural networks to perform spatial localization. - Christopher J. Cueva; Xue-Xin Wei (ICLR (Poster), 2018)

Recurrent computation in brains and machines. - Christopher J. Cueva (2019)

Emergence of functional and structural properties of the head direction system by optimization of recurrent neural networks. - Christopher J. Cueva; Peter Y. Wang; Matthew Chin; Xue-Xin Wei (ICLR, 2020)

Recurrent neural network models for working memory of continuous variables: activity manifolds, connectivity patterns, and dynamic codes. - Christopher J. Cueva; Adel Ardalan; Misha Tsodyks; Ning Qian (CoRR, 2021)

Baba Is AI: Break the Rules to Beat the Benchmark. - Nathan Cloos; Meagan Jens; Michelangelo Naim; Yen-Ling Kuo; Ignacio Cases; Andrei Barbu; Christopher J. Cueva (CoRR, 2024)

A Framework for Standardizing Similarity Measures in a Rapidly Evolving Field. - Nathan Cloos; Guangyu Robert Yang; Christopher J. Cueva (CoRR, 2024)

Emergent neural dynamics and geometry for generalization in a transitive inference task. - Kenneth Kay; Natalie Biderman; Ramin Khajeh; Manuel Beiran; Christopher J. Cueva; Daphna Shohamy; Greg Jensen; Xue-Xin Wei; Vincent P. Ferrera; L. F. Abbott (PLoS Comput. Biol., 2024)

Differentiable Optimization of Similarity Scores Between Models and Brains. - Nathan Cloos; Moufan Li; Markus Siegel; Scott L. Brincat; Earl K. Miller; Guangyu Robert Yang; Christopher J. Cueva (ICLR, 2025)

Can Biologically Plausible Temporal Credit Assignment Rules Match BPTT for Neural Similarity? E-prop as an Example. - Yuhan Helena Liu; Guangyu Robert Yang; Christopher J. Cueva (CoRR, 2025)