  In the brain, learning signals change over time and synaptic location, and
are applied based on the learning history at the synapse, in the complex
process of neuromodulation. Learning in artificial neural networks, on the
other hand, is shaped by hyper-parameters set before learning starts, which
remain static throughout learning, and which are uniform for the entire
network. In this work, we propose a method of deep artificial neuromodulation
which applies the concepts of biological neuromodulation to stochastic gradient
descent. Evolved neuromodulatory dynamics modify learning parameters at each
layer in a deep neural network over the course of the network's training. We
show that the same neuromodulatory dynamics can be applied to different models
and can scale to new problems not encountered during evolution. Finally, we
examine the evolved neuromodulation, showing that evolution found dynamic,
location-specific learning strategies.
