5 citations · 5 across the 3 of their papers we have counts for
3 papers
cs.LG2024
Dynamics of Supervised and Reinforcement Learning in the Non-Linear Perceptron
Christian Schmid, James M. Murray
The ability of a brain or a neural network to efficiently learn depends crucially on both the task structure and the learning rule. Previous works have analyzed the dynamical equat…
cs.LG2022
Gradient Descent Temporal Difference-difference Learning
Rong J. B. Zhu, James M. Murray
Off-policy algorithms, in which a behavior policy differs from the target policy and is used to gain experience for learning, have proven to be of great practical value in reinforc…
cs.NE2022★ 5 cited
Distinguishing Learning Rules with Brain Machine Interfaces
Jacob P. Portes, Christian Schmid, James M. Murray
Despite extensive theoretical work on biologically plausible learning rules, clear evidence about whether and how such rules are implemented in the brain has been difficult to obta…