  In living organisms, homeostasis is the natural regulation of internal states
aimed at maintaining conditions compatible with life. Typical artificial
systems are not equipped with comparable regulatory features. Here, we
introduce an artificial neural network that incorporates homeostatic features.
Its own computing substrate is placed in a needful and vulnerable relation to
the very objects over which it computes. For example, artificial neurons
performing classification of MNIST digits or Fashion-MNIST articles of clothing
may receive excitatory or inhibitory effects, which alter their own learning
rate as a direct result of perceiving and classifying the digits. In this
scenario, accurate recognition is desirable to the agent itself because it
guides decisions to regulate its vulnerable internal states and functionality.
Counterintuitively, the addition of vulnerability to a learner does not
necessarily impair its performance. On the contrary, self-regulation in
response to vulnerability confers benefits under certain conditions. We show
that homeostatic design confers increased adaptability under concept shift, in
which the relationships between labels and data change over time, and that the
greatest advantages are obtained under the highest rates of shift. This
necessitates the rapid un-learning of past associations and the re-learning of
new ones. We also demonstrate the superior abilities of homeostatic learners in
environments with dynamically changing rates of concept shift. Our homeostatic
design exposes the artificial neural network's thinking machinery to the
consequences of its own "thoughts", illustrating the advantage of putting one's
own "skin in the game" to improve fluid intelligence.
