Topic: Direct-Feedback-Alignment

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