University of Cambridge > Talks.cam > Inference Group > Dynamical Networks for Agent Systems

Dynamical Networks for Agent Systems

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Phil Cowans.

The basic ideas for a novel class of recurrent neural networks that learn through active interaction with an unknown environment by reading data and reward information and by emitting actions through its I/O interface. The network’s connections are fixed and no weights need to be adjusted. A neuron’s activity is determined by one of two possible boolean gates. Starting out with a uniform distribution over the two gates, learning consists in assigning a gate to each neuron as evidence is gathered, thus “programming” the network on-the-fly.

This talk is part of the Inference Group series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2020 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity