Truth or Consequences: Homeostatic Self-Regulation in Artificial Neural Networks

We assess existing attempts to build emotions and feelings in machines. We review our recently proposed design for machines possessing analogues of biological feeling. Key to our proposal is a homeostatic architecture that regulates internal states to maintain conditions compatible with life. In a first implementation of our design, we present results from a model of synaptic homeostasis in artificial neural networks. We introduce direct consequences to the network's function as a result of its own information processing activity. This model illustrates the benefits that may accrue to a homeostatic learner when it is placed in a needful and vulnerable relation to the objects over which it computes.