Sinapayen, Lana, Atsushi Masumori, Nathaniel Virgo, and Takashi Ikegami. “Learning by Stimulation Avoidance as a primary principle of spiking neural networks dynamics.” In Artificial Life Conference Proceedings 13 , pp. 175-182. One Rogers Street, Cambridge, MA 02142-1209 USA journals-info@ mit. edu: MIT Press, 2015.
Practical implementation of the concept of reward has deep implications on what artificial-life based systems can learn and how they learn it. How can a system distinguish between useful behavior and harmful behavior? In this paper we implement reward/punishment as the removal/application of a stimulation to a recurrent spiking neural network with spiketiming dependent plasticity. This implementation embodies the concept of reward at the level of the neuron, making learning mechanisms ubiquitous to the network. We show that this low-level learning scales up to the network level: the network learns arbitrary spatio-temporal firing patterns purely by interacting with the environment, from a random initial state where virtually no knowledge is available. This approach yields fast, noise-robust results.