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Christian Schmid

2 accepted papers

2024

Dynamics of Supervised and Reinforcement Learning in the Non-Linear Perceptron

NeurIPS 2024poster

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 equations describing learning in the relatively simplified context of the perceptron under assumptions of a student-teacher f…

Cited by 0SourcePDFScholar
2022

Distinguishing Learning Rules with Brain Machine Interfaces

NeurIPS 2022accept

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 obtain. We consider biologically plausible supervised- and reinforcement-learning rules and ask whether changes in network a…