Topic: neural tangent kernel

The learning rules implemented by neural networks in the brain are currently unknown. Understanding how candidate learning rules influence learned representations and prediction dynamics in neural networks could provide a link between experimentally accessible neural response measurements and possible underlying learning rules. To gain insight into how learning rules alter representation dynamics, we provide theoretical analysis of a broad class of learning rules which include exact gradient descent (GD), Feedback-Alignment (FA), Direct-Feedback-Alignment (DFA), Hebbian learning (Hebb) and gat