Learning in Recurrent Neural Networks

Explore the intricacies of learning in recurrent neural networks through this comprehensive lecture by Larry Abbott. Delve into topics such as continuous networks, effective connectivity, and spiking units. Gain insights into the philosophy and constraints of neural network learning, and understand the importance of multiple tests and model resets. Access additional exercises and references through the provided link to enhance your understanding of this complex subject. This lecture is part of a tutorial series founded by Emily Mackevicius and organized with the support of the BCS seminar committee and postdoc committee.

Intro
Overview
Continuous Networks
Recurrent Networks
Effective Connectivity
Philosophy
Constraints
Multiple tests
Spikes
Models reset
spiking units