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In living organisms, homeostasis is the natural regulation of internal statesaimed at maintaining conditions compatible with life. Typical artificialsystems are not equipped with comparable regulatory features. Here, weintroduce an artificial neural network that incorporates homeostatic features.Its own computing substrate is placed in a needful and vulnerable relation tothe very objects over which it computes. For example, artificial neuronsperforming classification of MNIST digits or Fashion-MNIST articles of clothingmay receive excitatory or inhibitory effects, which alter their own learningrate as a direct result of perceiving and classifying the digits. In thisscenario, accurate recognition is desirable to the agent itself because itguides decisions to regulate its vulnerable internal states and functionality.Counterintuitively, the addition of vulnerability to a learner does notnecessarily impair its performance. On the contrary, self-regulation inresponse to vulnerability confers benefits under certain conditions. We showthat homeostatic design confers increased adaptability under concept shift, inwhich the relationships between labels and data change over time, and that thegreatest advantages are obtained under the highest rates of shift. Thisnecessitates the rapid un-learning of past associations and the re-learning ofnew ones. We also demonstrate the superior abilities of homeostatic learners inenvironments with dynamically changing rates of concept shift. Our homeostaticdesign exposes the artificial neural network's thinking machinery to theconsequences of its own "thoughts", illustrating the advantage of putting one'sown "skin in the game" to improve fluid intelligence.

Need is All You Need: Homeostatic Neural Networks Adapt to Concept Shift