  A generic and scalable Reinforcement Learning scheme for Artificial Neural
Networks is presented, providing a general purpose learning machine. By
reference to a node threshold three features are described 1) A mechanism for
Primary Reinforcement, capable of solving linearly inseparable problems 2) The
learning scheme is extended to include a mechanism for Conditioned
Reinforcement, capable of forming long term strategy 3) The learning scheme is
modified to use a threshold-based deep learning algorithm, providing a robust
and biologically inspired alternative to backpropagation. The model may be used
for supervised as well as unsupervised training regimes.

Machine Learning;Neural and Evolutionary Computing