no code implementations • 1 Nov 2021 • Christopher J. Cueva, Adel Ardalan, Misha Tsodyks, Ning Qian
We find and characterize the connectivity patterns that support the Clifford torus.
no code implementations • ICLR 2020 • Christopher J. Cueva, Peter Y. Wang, Matthew Chin, Xue-Xin Wei
Overall, our results show that optimization of RNNs in a goal-driven task can recapitulate the structure and function of biological circuits, suggesting that artificial neural networks can be used to study the brain at the level of both neural activity and anatomical organization.
no code implementations • ICLR 2018 • Christopher J. Cueva, Xue-Xin Wei
As a new way to understand these neural representations, we trained recurrent neural networks (RNNs) to perform navigation tasks in 2D arenas based on velocity inputs.
1 code implementation • 9 Oct 2017 • Brian DePasquale, Christopher J. Cueva, Kanaka Rajan, G. Sean Escola, L. F. Abbott
We present a target-based method for modifying the full connectivity matrix of a recurrent network to train it to perform tasks involving temporally complex input/output transformations.
