203 citations · 375 across the 4 of their papers we have counts for
4 papers
Recurrent neural network models for working memory of continuous variables: activity manifolds, connectivity patterns, and dynamic codes
Christopher J. Cueva, Adel Ardalan, Misha Tsodyks +1
Many daily activities and psychophysical experiments involve keeping multiple items in working memory. When items take continuous values (e.g., orientation, contrast, length, loudn…
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 +1
Recent work suggests goal-driven training of neural networks can be used to model neural activity in the brain. While response properties of neurons in artificial neural networks b…
Emergence of grid-like representations by training recurrent neural networks to perform spatial localization
Christopher J. Cueva, Xue-Xin Wei
Decades of research on the neural code underlying spatial navigation have revealed a diverse set of neural response properties. The Entorhinal Cortex (EC) of the mammalian brain co…
full-FORCE: A Target-Based Method for Training Recurrent Networks
Brian DePasquale, Christopher J. Cueva, Kanaka Rajan +2
Trained recurrent networks are powerful tools for modeling dynamic neural computations. We present a target-based method for modifying the full connectivity matrix of a recurrent n…