ePoster · Machine Learning
Dynamics of Supervised and Reinforcement Learning in the Non-Linear Perceptron
Christian Schmid, James Murray · Bernstein Conference 2024
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 framework or a linearized output. While these assumptions have facilitated theoretical insights, they have precluded a detailed exploration of the roles of the nonlinearity and input-data distribution in determining the learning dynamics, limiting the applicability of the theories to real biological